The Complete Guide to AI Prompting & Engineering

Clarity and Specificity

Prompts should be clear, concise, and specific about the desired output.

Reduces ambiguity and guides the LLM to generate accurate and relevant responses.

Practical Applications:

• Clearly defining the task in the prompt.
• Specifying the output format (e.g., JSON, markdown).
• Using keywords to guide the LLM.

Historical Context: Evolved from early NLP research, where ambiguity often led to incorrect results. The need for clear instructions became apparent as models grew in complexity.

Contextual Awareness

Providing sufficient context in the prompt to help the LLM understand the domain and task.

Enables the LLM to generate contextually relevant and accurate responses.

Practical Applications:

• Including relevant background information.
• Providing examples of desired outputs.
• Using a conversational style to establish context.

Historical Context: Stemmed from the realization that LLMs are sensitive to the context provided, a crucial element in natural language understanding.

Iterative Refinement

The process of improving prompts based on the outputs generated by the LLM.

Iterative refinement is essential for achieving the desired results, as the first prompt is often not perfect.

Practical Applications:

• Analyzing the LLM's output for errors or inaccuracies.
• Adjusting the prompt to correct those issues.
• Experimenting with different phrasing and instructions.

Historical Context: Developed from software engineering practices, where iterative testing and refinement are fundamental to success. Adapted to prompting to fine-tune LLM outputs.

Zero-Shot Prompting

Asking the LLM to perform a task without providing any examples.

Best Practices:

• Use clear and direct instructions.
• Specify the desired output format.
• Avoid ambiguity.

Common Pitfalls:

• LLM may not understand complex or novel tasks without examples.
• Output can be unpredictable.

Example Prompts:

• Translate the following English text to French: 'Hello, how are you?'

• Summarize this article in three sentences: [Insert Article Here].

Use Cases:

• Simple text summarization.
• Basic translation tasks.
• Generating creative content based on a theme.

Few-Shot Prompting

Providing a few examples of the desired input-output pairs to guide the LLM.

Best Practices:

• Provide diverse and representative examples.
• Ensure the examples are high-quality and accurate.
• Use a consistent format for examples.

Common Pitfalls:

• LLM may overfit to the provided examples.
• Performance can be limited if the task is too different from the examples.

Example Prompts:

• Example 1: Input: 'The cat is on the mat.' Output: 'Le chat est sur le tapis.' Example 2: Input: 'The dog is in the park.' Output: 'Le chien est dans le parc.' Input: 'The bird is in the tree.' Output:

• Task: Classify the sentiment of these reviews. Example 1: Review: 'I loved this product!' Sentiment: 'Positive'. Example 2: Review: 'This was terrible.' Sentiment: 'Negative'. Review: 'It was okay.' Sentiment:

Use Cases:

• Complex text classification.
• Custom data transformations.
• Generating outputs in a specific style or format.

Chain-of-Thought Prompting

Encouraging the LLM to explain its reasoning process before providing the final answer.

Best Practices:

• Use phrases like 'Let's think step by step' or 'Explain your reasoning.'
• Encourage the LLM to break down complex problems into smaller steps.
• Review the reasoning process to ensure accuracy.

Common Pitfalls:

• LLM may generate incorrect reasoning.
• Responses can be verbose.

Example Prompts:

• Problem: If John has 5 apples and gives 2 to Mary, how many apples does John have left? Let's think step by step.

• Question: What is the capital of France? Explain your reasoning.

Use Cases:

• Solving complex reasoning tasks.
• Debugging code.
• Understanding the logic behind LLM's outputs.

Role Prompting

Assigning a specific role to the LLM to guide its behavior.

Best Practices:

• Clearly define the role and its responsibilities.
• Provide specific context related to the role.
• Use a consistent tone and style.

Common Pitfalls:

• LLM may struggle with poorly defined roles.
• Outputs may be inconsistent if the role is unclear.

Example Prompts:

• You are a professional marketing copywriter. Write a product description for a new smartphone.

• You are an expert tutor explaining algebra. Solve this equation and explain each step.

Use Cases:

• Generating content in a specific tone or style.
• Simulating different professional roles.
• Providing personalized user experiences.

Input-Output Template

Template:

Input: {input_text} Output: {output_format}

Components Explanation:

• input_text: The text or data that the LLM needs to process.
• output_format: The desired format of the response (e.g., summary, translation, classification).

When To Use:

• Simple translation or summarization tasks.
• When a basic input-output mapping is required.

Variations:

• Input: {input_data} Task: {task_description} Output: {output_format}

• Given: {context} Question: {question} Answer: {answer_format}

Example Implementations:

• Input: 'The quick brown fox jumps over the lazy dog.' Output: French translation

• Input: 'The weather today is sunny with a high of 75 degrees.' Output: Summary in one sentence.

Question-Answer Template

Template:

Question: {question} Answer: {answer_format}

Components Explanation:

• question: The question that needs to be answered.
• answer_format: The desired format of the answer (e.g., short answer, detailed explanation).

When To Use:

• When asking specific questions.
• When the desired output is a direct answer to a question.

Variations:

• Context: {context} Question: {question} Answer: {answer_format}

• Problem: {problem_statement} Solution: {solution_format}

Example Implementations:

• Question: What is the capital of Germany? Answer: City Name

• Question: Explain the theory of relativity in simple terms. Answer: Explanation

Chain-of-Thought Template

Template:

Let's think step by step. {problem_description} Therefore, {solution}

Components Explanation:

• problem_description: The problem or task that needs to be solved.
• solution: The final answer or solution.

When To Use:

• Complex reasoning tasks.
• When the LLM needs to break down the problem into steps.

Variations:

• First, {step1}. Then, {step2}. Finally, {solution}

• Explain your reasoning. {problem_description} Conclusion: {solution}

Example Implementations:

• Let's think step by step. If a train travels at 60 miles per hour for 2 hours, how far did it travel? Therefore, the train traveled 120 miles.

• Explain your reasoning. What is the square root of 144? Conclusion: The square root of 144 is 12.

Content Generation

Problem: Generate a blog post about the benefits of meditation for beginners.

Prompt Solution:

You are a wellness blogger. Write a blog post of about 500 words on the benefits of meditation for beginners. Include practical tips and advice.

Output Analysis: The LLM should generate a well-structured blog post with an introduction, body paragraphs, and practical tips.

Optimization Tips:

• Iterate on the prompt if the initial output is not satisfactory.
• Experiment with different tones and styles.
• Provide more specific instructions if needed.

Data Extraction

Problem: Extract product names and prices from a list of e-commerce descriptions.

Prompt Solution:

Extract the product name and price from the following list of e-commerce descriptions. Format the output as a JSON array. Descriptions: ['Product A - $20', 'Product B - $30', 'Product C - $40']

Output Analysis: The LLM should generate a valid JSON array with product names and prices.

Optimization Tips:

• Provide examples of the desired output format.
• Adjust the prompt based on the structure of the input data.
• Test on various edge cases to ensure robustness.

Code Generation

Problem: Generate Python code to sort a list of numbers in ascending order.

Prompt Solution:

Generate Python code to sort a list of numbers in ascending order. Include comments explaining each step.

Output Analysis: The LLM should generate correct Python code with comments explaining the logic.

Optimization Tips:

• Provide more specific requirements if needed.
• Test the generated code to ensure correctness.
• Experiment with different levels of detail in the prompt.

GPT-4

Unique Features: Strong reasoning capabilities, nuanced language understanding, and support for longer prompts.

Optimal Prompting Strategies:

• Use detailed and context-rich prompts.
• Utilize chain-of-thought prompting for complex tasks.
• Experiment with different personas and roles.
• Use few-shot learning with diverse examples.

Limitations:

• Can be sensitive to prompt phrasing.
• May generate verbose outputs.

Best Practices:

• Iteratively refine prompts based on outputs.
• Use clear and specific instructions.
• Test the model on various edge cases.

Bard

Unique Features: Good for creative writing, conversational tasks, and generating code in multiple languages.

Optimal Prompting Strategies:

• Use conversational prompts for dialogue-based tasks.
• Provide specific parameters for creative writing.
• Use clear instructions for code generation.
• Experiment with different tones and styles.

Limitations:

• May require more context for complex reasoning tasks.
• Can be prone to generating generic responses.

Best Practices:

• Use iterative prompting to refine outputs.
• Provide specific instructions for the desired style.
• Test the model on a variety of inputs.

Claude

Unique Features: Focus on harmless and helpful outputs, good for long-form content and summarization.

Optimal Prompting Strategies:

• Use clear and concise prompts.
• Provide context for summarization tasks.
• Utilize role prompts for different perspectives.
• Experiment with different levels of detail.

Limitations:

• May be less creative compared to other models.
• Can be strict with its safety guidelines.

Best Practices:

• Use iterative prompting to refine outputs.
• Provide clear instructions for the desired length.
• Test the model on a variety of inputs.

Text Summarization

Difficulty: Medium

Prompt Challenge: Summarize a given news article into three concise sentences using few-shot prompting. Provide two examples of article-summary pairs.

Starting Templates:

• Example 1: Article: [Article 1] Summary: [Summary 1]. Example 2: Article: [Article 2] Summary: [Summary 2]. Article: [New Article] Summary:

Solution Approaches:

• Start by providing clear and concise examples.
• Ensure the examples are representative of the task.
• Iterate on the prompt if the initial output is not satisfactory.

Evaluation Criteria:

• Accuracy of the summary.
• Conciseness of the summary.
• Relevance of the summary to the original article.

Code Generation

Difficulty: Hard

Prompt Challenge: Generate Python code to implement a simple calculator with addition, subtraction, multiplication, and division operations. Use chain-of-thought prompting to guide the LLM.

Starting Templates:

• Let's think step by step. Task: Generate Python code for a simple calculator with addition, subtraction, multiplication, and division. Therefore, the code should be:

Solution Approaches:

• Encourage the LLM to break down the problem into smaller steps.
• Use comments to explain the logic of the code.
• Test the generated code to ensure correctness.

Evaluation Criteria:

• Correctness of the generated code.
• Completeness of the code.
• Clarity of the code comments.

Creative Writing

Difficulty: Medium

Prompt Challenge: Write a short story about a robot who learns to feel emotions. Use role prompting to guide the LLM.

Starting Templates:

• You are a science fiction writer. Write a short story about a robot that learns to feel emotions. The story should be about 300 words.

Solution Approaches:

• Clearly define the role of the LLM as a science fiction writer.
• Specify the desired length and topic of the story.
• Encourage the LLM to use creative language and imagery.

Evaluation Criteria:

• Creativity and originality of the story.
• Quality of the writing.
• Adherence to the specified role and topic.

Customer Service Chatbot

A company used LLMs to create a customer service chatbot that can answer common questions, resolve issues, and provide personalized support. Few-shot learning was used to train the chatbot on common customer service scenarios.

Implementation Details:

Results: The chatbot reduced customer service response times and improved customer satisfaction rates. It also freed up human agents to handle more complex issues.

Lessons Learned:

• The importance of providing diverse and representative examples.
• The need for iterative refinement of prompts.
• The value of prompt engineering in creating effective chatbots.

Automated Content Creation

A marketing agency used LLMs to generate marketing copy, social media content, and blog posts. Role prompting was used to guide the LLM to generate content in different tones and styles.

Implementation Details:

Results: The agency significantly reduced content creation time and costs. The generated content was of high quality and met the needs of their clients.

Lessons Learned:

• The effectiveness of role prompting in generating diverse content.
• The importance of prompt templates in streamlining content creation.
• The need for iterative testing and refinement of prompts.

Clarity and Specificity

Ensuring prompts are precise and unambiguous to guide the LLM effectively.

Reduces ambiguity and improves the accuracy of LLM responses.

Practical Applications:

• Defining the task explicitly.
• Specifying the desired output format.
• Using clear, concise language.

Historical Context: Evolved from early AI interaction research focusing on instruction clarity.

Contextual Awareness

Providing sufficient background information to allow the LLM to generate relevant responses.

Helps the LLM understand the nuances of the task and the user's intent.

Practical Applications:

• Adding historical data or background information.
• Referencing previous turns in a conversation.
• Setting the scene or scenario for the LLM.

Historical Context: Developed with the rise of more context-aware AI models.

Iterative Refinement

The process of continuously adjusting prompts based on the LLM's output to achieve the desired result.

Allows for iterative improvements and optimization of the prompt.

Practical Applications:

• Analyzing initial responses.
• Modifying the prompt based on the analysis.
• Repeating this process until the desired output is achieved.

Historical Context: Derived from the scientific method, applied to AI model interaction.

Zero-Shot Prompting

Asking the LLM to perform a task without providing any examples.

Best Practices:

• Use clear and concise language.
• Specify the desired output format.
• Provide sufficient context.

Common Pitfalls:

• Ambiguous instructions.
• Lack of context.
• Expecting too much from a single prompt.

Example Prompts:

• Translate the following sentence to French: 'Hello, how are you?'

• Summarize this article in three sentences.

Use Cases:

• Simple translation.
• Basic summarization.
• Text generation with clear instructions.

Few-Shot Prompting

Providing a few examples to guide the LLM on how to perform the task.

Best Practices:

• Use diverse examples.
• Make examples clear and concise.
• Increase the number of examples if needed.

Common Pitfalls:

• Providing inconsistent examples.
• Using too few examples.
• Examples that are not representative of the task.

Example Prompts:

• Translate the following to Spanish:
  English: 'Hello'
  Spanish: 'Hola'
  English: 'Goodbye'
  Spanish: 'Adiós'
  English: 'Thank you'
  Spanish:

Use Cases:

• Complex translation.
• Text style transfer.
• Data extraction from specific formats.

Chain-of-Thought Prompting

Encouraging the LLM to explain its reasoning process step-by-step to solve complex problems.

Best Practices:

• Ask the model to 'think step by step'.
• Guide the model through the reasoning.
• Encourage detailed explanations.

Common Pitfalls:

• Overly simplistic reasoning.
• Missing intermediate steps.
• Incorrect assumptions.

Example Prompts:

• If a train leaves New York at 9 AM traveling at 60 mph, and another train leaves Chicago at 10 AM traveling at 75 mph, how many hours will it take for the two trains to meet? Think step by step.

Use Cases:

• Solving mathematical problems.
• Complex reasoning tasks.
• Debugging code.

Role-Playing Prompting

Instructing the LLM to adopt a specific role to generate responses from a particular perspective.

Best Practices:

• Clearly define the role.
• Provide context specific to the role.
• Encourage the LLM to maintain consistency with the role.

Common Pitfalls:

• Ambiguous role descriptions.
• Inconsistent role-playing by the LLM.
• Role is not relevant to the task.

Example Prompts:

• You are a historian. Explain the significance of the French Revolution.

• Act as a customer service representative. How can I help you today?

Use Cases:

• Generating diverse content.
• Simulating different scenarios.
• Improving customer service interactions.

Self-Consistency Prompting

Generating multiple responses from the LLM and selecting the most consistent one.

Best Practices:

• Generate several responses.
• Identify the most consistent response.
• Adjust prompt for better consistency if needed.

Common Pitfalls:

• Inconsistent responses.
• Difficulty in identifying the most consistent response.
• Computational overhead of generating multiple responses.

Example Prompts:

• Generate 5 different responses to the question: 'What is the capital of France?' and select the most common answer.

Use Cases:

• Ensuring accuracy in factual questions.
• Improving reliability in complex tasks.
• Verifying the correctness of a response.

Input-Output Template

Template:

Given the input: {input}, generate the output: {output}

Components Explanation:

• input: The input data or information provided to the LLM.
• output: The desired format or type of response from the LLM.

When To Use:

• Simple tasks with clear input and output requirements.
• Tasks requiring structured output.
• Data transformation tasks.

Variations:

• Adding specific instructions for output format.

• Including examples of input-output pairs.

• Specifying constraints on the output.

Example Implementations:

• Given the input: 'The cat sat on the mat', generate the output: 'Noun: cat, Verb: sat, Preposition: on, Noun: mat'

• Given the input: '12345', generate the output: 'The sum of all digits is 15'

Question-Answering Template

Template:

Given the context: {context}, answer the question: {question}

Components Explanation:

• context: The background information or data necessary to answer the question.
• question: The specific question the LLM should answer.

When To Use:

• Information retrieval tasks.
• Answering questions based on given text.
• Fact verification tasks.

Variations:

• Adding multiple contexts.

• Specifying the output format for the answer.

• Requiring citations or references.

Example Implementations:

• Given the context: 'The capital of France is Paris', answer the question: 'What is the capital of France?'

• Given the context: 'The quick brown fox jumps over the lazy dog', answer the question: 'What is the subject of the sentence?'

Role-Based Template

Template:

You are a {role}. Your task is to {task}. {additional_instructions}

Components Explanation:

• role: The persona or role the LLM should adopt.
• task: The specific action or task the LLM needs to perform.
• additional_instructions: Any further requirements or guidelines.

When To Use:

• Generating content from a specific perspective.
• Simulating real-world interactions.
• Creative writing tasks.

Variations:

• Adding constraints on the role's behavior.

• Defining specific tone or style for the role.

• Providing background information for the role.

Example Implementations:

• You are a travel agent. Your task is to recommend a vacation package to Hawaii. Include details about flights, hotels, and activities.

• You are a software engineer. Your task is to explain the concept of object-oriented programming. Use simple examples.

Summarizing a Research Paper

Problem: Given a lengthy research paper, generate a concise summary highlighting the key findings and contributions.

Prompt Solution:

Summarize the following research paper in three bullet points, focusing on the main findings: {research_paper_text}

Output Analysis: The summary should be concise, accurate, and highlight the main findings of the paper.

Optimization Tips:

• Specify the length of the summary.
• Use keywords to guide the LLM.
• Provide examples of summaries if needed.

Generating a Marketing Slogan

Problem: Create a catchy and memorable slogan for a new product.

Prompt Solution:

Generate three marketing slogans for a new mobile phone that focuses on 'innovation' and 'user-friendliness'.

Output Analysis: The slogans should be creative, memorable, and aligned with the product's attributes.

Optimization Tips:

• Specify the length of the slogans.
• Provide examples of successful marketing slogans.
• Use keywords that reflect the product's identity.

Translating Technical Documentation

Problem: Translate a technical document from English to Spanish, ensuring accuracy and clarity.

Prompt Solution:

Translate the following technical document from English to Spanish. Ensure the translation is accurate and maintains the original technical meaning: {technical_document_text}

Output Analysis: The translated document should be accurate, clear, and maintain the technical meaning.

Optimization Tips:

• Provide a glossary of technical terms.
• Use few-shot prompting with examples of technical translations.
• Specify the target audience for the translation.

GPT-4

Unique Features: Advanced reasoning, high context length, superior language understanding.

Optimal Prompting Strategies:

• Use more complex prompts.
• Leverage chain-of-thought prompting for complex reasoning.
• Use multi-step prompts for nuanced tasks.

Limitations:

• High cost for extensive use.
• Potential for overly verbose responses.
• Can still make factual errors.

Best Practices:

• Use precise instructions.
• Break down complex tasks into smaller steps.
• Iteratively refine prompts for best results.

Llama 2

Unique Features: Open-source model, customizable, suitable for fine-tuning.

Optimal Prompting Strategies:

• Provide more examples in few-shot prompting.
• Use clear and explicit instructions.
• Adjust prompts based on specific fine-tuning data.

Limitations:

• May require more examples for complex tasks.
• Can be less accurate than closed-source models.
• Performance varies with different fine-tuning approaches.

Best Practices:

• Use a variety of examples in prompts.
• Be precise with instructions.
• Iterate with fine-tuning to improve performance.

Bard

Unique Features: Google's conversational AI, strong at creative writing and idea generation.

Optimal Prompting Strategies:

• Use creative prompts.
• Ask for multiple different ideas.
• Experiment with different tones and styles.

Limitations:

• Can sometimes be less precise on factual topics.
• May need additional guidance for technical subjects.
• Can be verbose if not specifically instructed.

Best Practices:

• Use explicit prompts for factual tasks.
• Give clear instructions for tone and style.
• Use multiple prompts for complex tasks.

Text Generation

Difficulty: Medium

Prompt Challenge: Generate a short story about a robot discovering its emotions.

Starting Templates:

• You are a robot. Write a story about...

• Use the input: {robot_description}, to generate a story about emotions.

Solution Approaches:

• Start with a basic prompt and iteratively add details.
• Use role-playing to guide the robot's character.
• Apply chain-of-thought prompting to develop the plot.

Evaluation Criteria:

• Creativity of the story.
• Clarity of the narrative.
• Consistency of the robot's character.

Problem Solving

Difficulty: Hard

Prompt Challenge: Solve the following logic puzzle using chain-of-thought prompting: There are 5 houses in a row, each painted a different color. In each house lives a person of a different nationality. These 5 owners drink a certain type of beverage, smoke a certain brand of cigar, and keep a certain pet. No owners have the same pet, smoke the same brand of cigar, or drink the same beverage. The British man lives in the red house. The Swedish man keeps dogs as pets. The Danish man drinks tea. The green house is on the left of the white house. The green house's owner drinks coffee. The person who smokes Pall Mall raises birds. The owner of the yellow house smokes Dunhill. The man living in the center house drinks milk. The Norwegian lives in the first house. The man who smokes Blends lives next to the one who keeps cats. The man who keeps horses lives next to the man who smokes Dunhill. The owner who smokes BlueMaster drinks beer. The German smokes Prince. The Norwegian lives next to the blue house. The man who smokes Blends has a neighbor who drinks water. Who owns the fish?

Starting Templates:

• Solve the following logic puzzle step by step: {puzzle}

• Use chain-of-thought prompting to solve the following: {puzzle}

Solution Approaches:

• Break down the problem into smaller logical steps.
• Use chain-of-thought to track the deductions.
• Use a structured format to organize the information.

Evaluation Criteria:

• Accuracy of the solution.
• Clarity of the reasoning process.
• Correct application of chain-of-thought prompting.

Code Generation

Difficulty: Medium

Prompt Challenge: Generate Python code to calculate the factorial of a given number using recursion.

Starting Templates:

• Write a Python function to calculate the factorial of a number using recursion.

• Generate a Python code that uses recursion to calculate factorial of {number}.

Solution Approaches:

• Start with a basic prompt and add constraints.
• Use few-shot prompting to guide the code generation.
• Test the code and adjust the prompt if needed.

Evaluation Criteria:

• Correctness of the code.
• Efficiency of the code.
• Clarity of the code.

AI-Powered Customer Support

An e-commerce company uses LLMs to automate customer support, reducing response times and improving customer satisfaction.

Implementation Details:

Outcomes: Reduced customer support costs by 40%. Average response time decreased from 24 hours to 5 minutes. Customer satisfaction scores increased by 15%.

Lessons Learned:

Automated Content Creation

A marketing agency uses LLMs to generate marketing content, including blog posts, social media updates, and ad copy.

Implementation Details:

Outcomes: Content creation time reduced by 60%. Content output increased by 80%. Marketing campaign performance improved by 20%.

Lessons Learned:

Clarity and Specificity

The practice of crafting prompts that are unambiguous and precisely define the desired output.

Reduces ambiguity, leading to more accurate and consistent responses from LLMs.

Practical Applications:

• Clearly defining the role of the LLM.
• Specifying the output format (e.g., JSON, bullet points).
• Providing context and constraints.

Historical Context: Early work in NLP showed that vague instructions led to unpredictable results. The need for precise instructions became evident as models became more complex.

Contextual Awareness

Understanding and incorporating relevant context into prompts to guide the LLM's response.

Allows LLMs to generate more relevant and meaningful outputs by understanding the situation.

Practical Applications:

• Providing background information.
• Referencing previous interactions.
• Setting the scene or scenario.

Historical Context: The evolution of language models has emphasized the importance of context in generating coherent and contextually appropriate text.

Iterative Refinement

The process of iteratively improving prompts based on the LLM's response.

Enables the fine-tuning of prompts to achieve the desired output quality and accuracy.

Practical Applications:

• Analyzing the LLM's initial response.
• Adjusting the prompt based on the analysis.
• Repeating the process until the output is satisfactory.

Historical Context: This is a core concept in software development that has been adapted for prompting, highlighting the value of continuous feedback.

Zero-Shot Prompting

Asking the LLM to perform a task without providing any examples.

Best Practices:

• Use clear and concise instructions.
• Define the desired output format.
• Avoid ambiguity in the prompt.

Common Pitfalls:

• LLM may not understand the task without examples.
• Output may be unpredictable or inaccurate.
• Requires strong understanding of the LLM's capabilities.

Example Prompts:

• Translate the following sentence into French: 'Hello, how are you?'

• Summarize the following article in three bullet points: [article text]

• Classify the sentiment of the following text as positive, negative, or neutral: 'This is a great day!'

Use Cases:

• Simple translation tasks.
• Basic summarization.
• Sentiment analysis.
• Content generation.

Few-Shot Prompting

Providing the LLM with a few examples of the desired input-output pairs to guide its responses.

Best Practices:

• Use diverse examples that cover the range of inputs.
• Ensure the examples are high quality and correct.
• Clearly label the input and output in the examples.

Common Pitfalls:

• Examples might bias the model.
• Incorrect examples can lead to poor results.
• Requires careful selection of examples.

Example Prompts:

• Input: 'apple', Output: 'fruit'. Input: 'carrot', Output: 'vegetable'. Input: 'chair', Output:

• Input: 'The movie was amazing', Output: 'Positive'. Input: 'I hated the book', Output: 'Negative'. Input: 'The food was okay', Output:

• Input: '2 + 2 = 4', Output: 'Correct'. Input: '5 * 3 = 15', Output: 'Correct'. Input: '10 - 5 = 2', Output:

Use Cases:

• Complex classification tasks.
• Code generation.
• Creative text generation.
• Data extraction.

Chain-of-Thought Prompting

Encouraging the LLM to break down the problem into a series of steps, explaining its reasoning process.

Best Practices:

• Clearly instruct the LLM to show its reasoning.
• Use phrases like 'Let's think step by step'.
• Encourage logical and coherent explanations.

Common Pitfalls:

• LLM may produce incorrect reasoning.
• Can be more verbose and slower.
• Requires careful prompt engineering.

Example Prompts:

• Solve this math problem and explain your reasoning step by step: 'If a train travels 120 miles in 2 hours, what is its speed?'

• Explain the process of photosynthesis step by step.

• Describe the steps involved in writing a research paper. Let's think step by step.

Use Cases:

• Complex reasoning problems.
• Mathematical calculations.
• Logical deduction tasks.
• Process explanations.

Role Prompting

Instructing the LLM to adopt a specific role or persona, influencing the style and content of its responses.

Best Practices:

• Clearly define the role and its characteristics.
• Use specific language and tone.
• Provide context relevant to the role.

Common Pitfalls:

• LLM may not fully embody the role.
• Can lead to biased or stereotypical responses.
• Requires careful role definition.

Example Prompts:

• Act as a professional chef and provide a recipe for a vegetarian lasagna.

• Assume the role of a historian and explain the causes of World War II.

• You are a customer service representative. Respond to the following customer complaint: 'My order was delayed.'

Use Cases:

• Creative writing.
• Customer service simulations.
• Educational content generation.
• Role-playing scenarios.

Output Format Prompting

Specifying the desired output format (e.g., JSON, XML, CSV, bullet points) to structure the LLM's response.

Best Practices:

• Clearly define the output schema or structure.
• Provide examples of the expected format.
• Use specific formatting instructions.

Common Pitfalls:

• LLM may not always adhere to the format.
• Requires careful formatting instructions.
• Can be challenging with complex formats.

Example Prompts:

• Extract the following information and output it in JSON format: [text]

• Generate a list of the top 10 movies of 2023 in bullet points.

• Create a CSV file with the following data: [data]

Use Cases:

• Data extraction and transformation.
• Structured data generation.
• API integration.
• Report generation.

Input-Output Pair Pattern

Template:

Input: [Input text] Output: [Output text]

Components Explanation:

• Input: The text or data provided to the LLM.
• Output: The desired response or result.

When To Use:

• When demonstrating desired behavior with examples.
• For few-shot prompting tasks.
• When the relationship between input and output is clear.

Variations:

• Multiple input-output pairs.

• Input-output pairs with different formats.

• Input-output pairs with intermediate steps.

Example Implementations:

• Input: 'cat' Output: 'animal' Input: 'tree' Output: 'plant' Input: 'table' Output:

Question-Answering Pattern

Template:

Question: [Question text] Answer: [Answer text]

Components Explanation:

• Question: The query or request posed to the LLM.
• Answer: The response or solution provided by the LLM.

When To Use:

• When seeking specific answers to questions.
• For information retrieval tasks.
• When the output is a factual or descriptive answer.

Variations:

• Multiple questions and answers.

• Questions with specific context.

• Questions requiring reasoning and inference.

Example Implementations:

• Question: 'What is the capital of France?' Answer: 'Paris'

• Question: 'Who is the president of the United States?' Answer:

Instruction-Follow Pattern

Template:

Instruction: [Instruction text] Response: [Response text]

Components Explanation:

• Instruction: The specific task or command given to the LLM.
• Response: The LLM's output based on the instruction.

When To Use:

• When providing specific directions for the LLM to follow.
• For tasks requiring specific formatting or style.
• When the output is a result of a procedural instruction.

Variations:

• Multiple instructions.

• Instructions with conditional logic.

• Instructions with specific constraints.

Example Implementations:

• Instruction: 'Summarize the following article in 3 sentences.' Response: [Summary]

• Instruction: 'Translate the following sentence to Spanish.' Response: [Translation]

Chain-of-Thought Pattern

Template:

Let's think step by step. [Task description] [reasoning steps] [final answer]

Components Explanation:

• Task description: The problem or question to be solved.
• reasoning steps: The intermediate steps in solving the problem.
• final answer: The final response to the task.

When To Use:

• For complex reasoning tasks.
• When requiring explanation and justification.
• When the output is a result of logical steps.

Variations:

• Varying the number of steps.

• Using different reasoning techniques.

• Providing additional context or constraints.

Example Implementations:

• Let's think step by step. If a car travels at 60 miles per hour for 3 hours, how far does it travel? First, we need to multiply the speed by the time. 60 miles/hour * 3 hours = 180 miles. The car travels 180 miles.

Customer Service Chatbot

Problem: Develop a chatbot that can answer common customer questions about order status, shipping, and returns.

Prompt Solution:

You are a customer service chatbot. A customer asks: 'Where is my order #12345?' Please provide the current status and estimated delivery date. If the order cannot be found, respond appropriately.

Output Analysis: The chatbot should provide a clear and concise response. If the order is found, it should provide the status and delivery date. If the order is not found, it should inform the user appropriately.

Optimization Tips:

• Use few-shot examples to guide responses.
• Implement error handling for invalid order numbers.
• Use a structured format (e.g., JSON) for easier parsing.

Content Generation for Blogs

Problem: Generate a blog post on the benefits of regular exercise, including specific benefits and actionable tips.

Prompt Solution:

Write a blog post about the benefits of regular exercise. Include at least 3 specific benefits supported by data, and provide 5 actionable tips for beginners to start exercising. The blog post should be about 500 words and include headings and subheadings for clarity.

Output Analysis: The blog post should be well-structured, informative, and engaging. It should include specific benefits, actionable tips, and be easy to read.

Optimization Tips:

• Use role prompting to adopt a professional tone.
• Provide a detailed outline for the blog post.
• Iteratively refine the prompt based on the initial output.

Data Extraction from Documents

Problem: Extract key information such as names, addresses, and phone numbers from a given text document.

Prompt Solution:

Extract all names, addresses, and phone numbers from the following text document and output it in JSON format: [text document]

Output Analysis: The extracted data should be accurate and complete. The JSON format should be valid and easy to parse.

Optimization Tips:

• Use few-shot examples with different formats of text.
• Handle variations in formatting within the document.
• Use regular expressions in prompts for better accuracy.

GPT-4

Unique Features: Advanced reasoning, complex task handling, high-quality text generation, multi-modal input.

Optimal Prompting Strategies:

• Use detailed and specific prompts.
• Leverage few-shot examples with complex relationships.
• Utilize chain-of-thought prompting for intricate problems.
• Experiment with role-playing and persona-based prompts.
• Use output format prompts for structured data.

Limitations:

• Can be expensive to use for large-scale tasks.
• May sometimes generate verbose responses.
• May require significant fine-tuning for specific use cases.

Best Practices:

• Start with simple prompts and gradually increase complexity.
• Iteratively refine prompts based on the output.
• Use temperature parameter to control randomness in output.
• Test multiple prompt variations.

Bard

Unique Features: Strong conversational abilities, creative content generation, integration with Google services.

Optimal Prompting Strategies:

• Utilize natural language prompts for conversational tasks.
• Leverage few-shot examples for creative content generation.
• Use role-playing and storytelling prompts.
• Utilize output format prompts for structured output.
• Use Google services to complement LLM responses.

Limitations:

• May sometimes hallucinate or generate inaccurate information.
• Requires careful prompt engineering for specific use cases.
• May not perform as well on complex reasoning tasks compared to GPT-4.

Best Practices:

• Start with natural language and conversational prompts.
• Iteratively refine prompts based on feedback.
• Use a variety of prompt styles to explore model capabilities.
• Test different prompt variations.

Llama 2

Unique Features: Open-source model with customizability, fine-tuning capabilities, and strong performance.

Optimal Prompting Strategies:

• Leverage few-shot prompting for specific tasks.
• Utilize chain-of-thought prompting for complex reasoning.
• Fine-tune the model on task-specific datasets.
• Use specific prompts to control output style.
• Combine prompting with fine-tuning for better results.

Limitations:

• May require more effort to set up and run.
• Performance may vary depending on the fine-tuning and dataset used.
• Requires some understanding of model architecture and training.

Best Practices:

• Start with a strong base model and fine-tune on specific tasks.
• Use datasets that are relevant to the use case.
• Carefully monitor and evaluate model performance.
• Experiment with different hyperparameters and fine-tuning techniques.

Prompt Design

Difficulty: Medium

Prompt Challenge: Design a prompt to generate a short story about a robot who learns to feel emotions. The story should be at least 300 words and include a beginning, middle, and end. The robot should experience at least 3 different emotions.

Starting Templates:

• Write a story about a robot...

• The robot's name is...

• The story should include the emotions...

Solution Approaches:

• Use role-playing to define the robot's character.
• Use chain-of-thought prompting to develop the plot.
• Use specific formatting to structure the story.
• Iteratively refine the prompt to achieve the desired output.

Evaluation Criteria:

• Clarity and coherence of the story.
• Appropriate use of emotions.
• Structure of the story (beginning, middle, end).
• Creativity and originality.

Prompt Optimization

Difficulty: Hard

Prompt Challenge: Optimize a prompt to extract product names, prices, and descriptions from a given e-commerce website's product page. The output should be in JSON format, and the prompt should handle variations in page structure and formatting.

Starting Templates:

• Extract product information from...

• Output the information in JSON format...

• The JSON should include fields for product name, price, and description

Solution Approaches:

• Use few-shot examples with different product page layouts.
• Use output format prompting to define the JSON schema.
• Iteratively refine the prompt to handle variations in formatting.
• Consider using regular expressions in the prompt for better accuracy.

Evaluation Criteria:

• Accuracy of extracted information.
• Correctness of the JSON output.
• Ability to handle variations in page structure.
• Completeness of the extracted data.

Chain-of-Thought Application

Difficulty: Medium

Prompt Challenge: Use chain-of-thought prompting to solve a complex word problem. Explain each step of the reasoning process and provide the final answer.

Starting Templates:

• Let's think step by step. Solve the following word problem...

• First, we need to...

• Next, we should...

• Finally, the answer is...

Solution Approaches:

• Break the word problem into smaller steps.
• Guide the LLM to use logical reasoning.
• Ensure each step is clearly explained.
• Provide the final answer based on the reasoning steps.

Evaluation Criteria:

• Clarity and accuracy of each reasoning step.
• Logical flow of the reasoning process.
• Correctness of the final answer.
• Completeness of the explanation.

Automated Content Creation for Marketing

A marketing team used advanced prompting techniques to automate the creation of social media posts, blog articles, and email newsletters. This significantly reduced the time and effort required for content creation, while maintaining high quality and consistency.

Lessons Learned:

AI-Powered Customer Support System

A customer support team implemented an AI-powered chatbot that uses advanced prompting techniques to provide 24/7 support to customers. This helped to reduce response times and improve customer satisfaction.

Lessons Learned:

Clarity and Specificity

The principle that prompts should be clear, concise, and specific about the desired task and format of the response.

Reduces ambiguity and ensures the model understands what is expected, leading to more accurate and useful outputs.

Practical Applications:

• Use precise verbs and nouns.
• Specify the output format (e.g., JSON, Markdown).
• Avoid vague or open-ended questions.

Historical Context: Evolved from the early days of NLP, where ambiguity was a major challenge, to modern LLMs that are sensitive to nuances in language.

Contextual Awareness

The understanding that LLMs respond based on the context provided in the prompt, including instructions, examples, and background information.

Provides necessary information to guide the model towards the desired response, especially for complex or nuanced tasks.

Practical Applications:

• Include relevant background information.
• Provide examples of expected outputs.
• Use delimiters to separate different parts of the prompt.

Historical Context: Became increasingly important as models grew in size and complexity, requiring more structured input to guide their behavior.

Iterative Refinement

The process of refining prompts based on the model's responses to improve the quality and relevance of the output.

Iterative refinement is crucial for optimizing prompt effectiveness; it allows for fine-tuning the prompt based on the LLM's behavior.

Practical Applications:

• Analyze the model's output and identify areas for improvement.
• Adjust the prompt based on the analysis.
• Repeat the process until the desired output is achieved.

Historical Context: A core practice for any LLM interaction, recognizing that the initial prompt is rarely perfect and requires fine-tuning.

Zero-Shot Prompting

Asking the model to perform a task without providing any examples. The model relies solely on its pre-existing knowledge.

Best Practices:

• Use clear and specific instructions.
• Avoid ambiguous language.
• Test with various prompts to see what works best.

Common Pitfalls:

• Can struggle with complex or unfamiliar tasks.
• May produce less accurate results than few-shot prompting.
• Requires a well-defined prompt to get meaningful results.

Example Prompts:

• Translate 'hello' to Spanish.

• Summarize the following article in three sentences: [article text].

• Write a short poem about the ocean.

Use Cases:

• Simple translation.
• Basic summarization.
• Generating creative text formats.

Few-Shot Prompting

Providing a few examples of the desired input-output pairs to guide the model to perform the task. This helps the model understand the task better.

Best Practices:

• Use diverse and representative examples.
• Keep examples clear and concise.
• Ensure the examples directly relate to the desired task.

Common Pitfalls:

• Poorly chosen examples can lead to incorrect outputs.
• Too many examples may not always improve the performance.
• Requires careful selection of examples to be effective.

Example Prompts:

• Translate the following from English to French:
  English: 'Hello'
  French: 'Bonjour'
  English: 'Goodbye'
  French: 'Au revoir'
  English: 'Thank you'
  French:

• Classify the sentiment:
  Review: 'I loved this movie!'
  Sentiment: Positive
  Review: 'This is terrible.'
  Sentiment: Negative
  Review: 'It was okay.'
  Sentiment:

Use Cases:

• Complex translation tasks.
• Sentiment analysis.
• Text classification.
• Code generation.

Chain-of-Thought Prompting

Encouraging the model to explain its reasoning process step-by-step, often leading to more accurate and reliable results, especially for complex tasks.

Best Practices:

• Use 'Let's think step by step' or similar phrases.
• Guide the model to break down the problem into smaller steps.
• Encourage detailed explanations for each step.

Common Pitfalls:

• May increase the length of the output.
• Can sometimes produce verbose and unnecessary steps.
• Requires careful prompt design to ensure relevant steps are included.

Example Prompts:

• Solve the following math problem: If a train leaves New York at 8:00 AM traveling at 60 mph and another train leaves Los Angeles at 9:00 AM traveling at 70 mph, when will they meet? Let's think step by step.

• Explain how photosynthesis works. Let's think step by step.

Use Cases:

• Complex problem-solving.
• Mathematical reasoning.
• Scientific explanations.

Role Prompting

Instructing the model to assume a specific role or persona, which can guide the style and content of its responses.

Best Practices:

• Clearly define the role and its characteristics.
• Use specific language and tone associated with the role.
• Provide examples of how the role would respond.

Common Pitfalls:

• May result in overly stylized or inaccurate responses.
• Requires a well-defined role to be effective.
• Can sometimes lead to inconsistent results if the role is not clear.

Example Prompts:

• You are a helpful and knowledgeable travel agent. Recommend a vacation spot in Europe for a family with young children.

• You are a seasoned software engineer. Explain the concept of polymorphism in object-oriented programming.

Use Cases:

• Generating creative content with specific styles.
• Simulating different personas for dialogues.
• Providing expert advice in various domains.

Output Format Specification

Explicitly defining the desired output format (e.g., JSON, XML, Markdown) to ensure the model produces structured and parsable data.

Best Practices:

• Clearly specify the format using keywords.
• Provide examples of the desired format.
• Use delimiters to separate different parts of the output.

Common Pitfalls:

• May not always produce perfectly formatted output.
• Requires careful specification of the desired format.
• Can sometimes result in unexpected formatting if the prompt is not precise.

Example Prompts:

• Generate a JSON object containing the name, age, and city of a person. Example: {"name": "John", "age": 30, "city": "New York"}. Now create a similar JSON object for a different person.

• Provide the following information in Markdown table format: Name, Age, City. Create a table for two individuals.

Use Cases:

• Data extraction and transformation.
• Generating structured reports.
• Creating configuration files.

Question-Answering Pattern

Template:

Given the context: [context], answer the question: [question]

Components Explanation:

• context: The background information or text that the model should reference.
• question: The specific query to be answered based on the context.

When To Use:

• Retrieval-based question-answering.
• Extracting specific information from a document.
• Answering factual questions based on provided data.

Variations:

• Adding constraints on the answer length.

• Asking for a specific type of answer (e.g., a list, a summary).

• Using few-shot examples to guide the answer format.

Example Implementations:

• Given the context: 'The quick brown fox jumps over the lazy dog.' answer the question: What animal jumps?

• Given the context: 'The capital of France is Paris.' Answer the question: What is the capital of France in one word?

Comparative Analysis Pattern

Template:

Compare and contrast [item1] and [item2] based on [criteria]

Components Explanation:

• item1: The first item to be compared.
• item2: The second item to be compared.
• criteria: The attributes or aspects on which the comparison should be based.

When To Use:

• Analyzing the similarities and differences between two or more items.
• Evaluating the pros and cons of different options.
• Highlighting key differences between competing products or concepts.

Variations:

• Using a table to present the comparison.

• Adding a summary of the key differences.

• Providing a conclusion based on the comparison.

Example Implementations:

• Compare and contrast Python and Java based on performance, ease of use, and community support.

• Compare and contrast the features of a sedan and an SUV focusing on cargo space and fuel efficiency.

Transformation Pattern

Template:

Convert the following [input_type] to [output_type]: [input_data]

Components Explanation:

• input_type: The format or type of the input data.
• output_type: The desired format or type of the output data.
• input_data: The data to be transformed.

When To Use:

• Converting text from one format to another (e.g., HTML to Markdown).
• Summarizing a document into a shorter version.
• Translating text from one language to another.

Variations:

• Specifying the length or format of the output.

• Providing additional constraints on the transformation.

• Using few-shot examples to guide the transformation process.

Example Implementations:

• Convert the following HTML to Markdown: <h1>Title</h1><p>Paragraph</p>

• Summarize the following article in three sentences: [article text]

• Translate the following English text to Spanish: Hello, how are you?

Automated Content Creation

Problem: Generate a blog post on the benefits of using AI in education.

Prompt Solution:

Write a detailed blog post on the benefits of using AI in education. Include examples of AI tools and their applications. Organize the post into sections with clear headings and subheadings.

Output Analysis: The model should generate a well-structured blog post with relevant examples and clear explanations. The output should be easy to read and understand.

Optimization Tips:

• Use few-shot examples of blog posts to guide the format.
• Specify the tone and style of the writing.
• Ask the model to include a call to action at the end of the post.

Customer Support Chatbot

Problem: Develop a chatbot that can answer common customer queries about product returns.

Prompt Solution:

You are a customer support chatbot. Respond to the following customer query about returns: [customer query]. Provide clear and concise answers based on the provided return policy. Return Policy: [return policy details]

Output Analysis: The model should provide accurate and helpful responses based on the return policy. The output should be clear and easy for customers to understand.

Optimization Tips:

• Use few-shot examples of customer support interactions.
• Instruct the model to ask clarifying questions if needed.
• Ask the model to provide links to relevant resources.

Code Generation

Problem: Generate Python code to read a CSV file and calculate the average of a specific column.

Prompt Solution:

Generate Python code to read a CSV file named 'data.csv' and calculate the average of the column named 'values'. Include error handling for file not found and invalid column name. Provide comments for each step.

Output Analysis: The model should generate functional Python code with error handling and comments. The code should be easy to understand and modify.

Optimization Tips:

• Use few-shot examples of Python code.
• Specify the required libraries and packages.
• Ask the model to include unit tests for the code.

GPT-4

Unique Features: Advanced reasoning capabilities, improved contextual understanding, and enhanced creative writing abilities.

Optimal Prompting Strategies:

• Use chain-of-thought prompting for complex tasks.
• Provide detailed and specific instructions.
• Experiment with different prompt variations to find the best approach.

Limitations:

• Higher cost per token compared to other models.
• Can be slower for processing large inputs.
• May still produce inaccurate results if the prompt is not well-defined.

Best Practices:

• Use clear and concise prompts.
• Provide context and examples when necessary.
• Iteratively refine prompts based on the model's outputs.

Claude

Unique Features: Known for generating longer, more coherent, and conversational text with a focus on safety and ethical considerations.

Optimal Prompting Strategies:

• Use detailed instructions and examples.
• Encourage the model to explain its reasoning process.
• Leverage its ability to handle long-form content.

Limitations:

• May be more verbose compared to other models.
• Can sometimes be overly cautious in its responses.
• Requires careful prompt design to ensure it stays on topic.

Best Practices:

• Provide clear context and purpose for the task.
• Use delimiters to separate different parts of the prompt.
• Experiment with different prompting styles to find what works best.

Llama 2

Unique Features: Open-source model that offers flexibility and customization, with a focus on balancing performance and efficiency.

Optimal Prompting Strategies:

• Use specific instructions and examples.
• Test with various prompt variations to find the optimal approach.
• Utilize its ability to handle a wide range of tasks.

Limitations:

• May require more fine-tuning compared to closed-source models.
• Performance may vary depending on the specific task and context.
• Can sometimes produce less coherent outputs compared to GPT-4 or Claude.

Best Practices:

• Focus on clear and concise instructions.
• Provide a variety of examples to guide the model.
• Iteratively refine prompts based on the model's outputs.

Text Summarization

Difficulty: Medium

Prompt Challenge: Summarize the following article in three sentences. The summary should capture the main points of the article. Article:[Article Text]

Starting Templates:

• Summarize the following article in three sentences: [article text]

• Provide a concise summary of the article in three sentences: [article text]

• In three sentences, summarize the main points of this article: [article text]

Solution Approaches:

• Use zero-shot prompting to start with.
• If needed, refine the prompt with few-shot examples of summaries.
• Experiment with different prompt variations to find the most effective one.

Evaluation Criteria:

• The summary should accurately reflect the main points of the article.
• The summary should be concise and limited to three sentences.
• The summary should be clear and easy to understand.

Creative Writing

Difficulty: Medium

Prompt Challenge: Write a short story about a robot learning to feel emotions. The story should have a clear beginning, middle, and end.

Starting Templates:

• Write a short story about a robot learning to feel emotions.

• Create a fictional story about a robot experiencing emotions for the first time.

• Develop a story about a robot's journey to understand human feelings.

Solution Approaches:

• Use role prompting to guide the model to write a story.
• Provide examples of short stories to guide the format.
• Use iterative refinement to improve the story's plot and characters.

Evaluation Criteria:

• The story should have a clear beginning, middle, and end.
• The story should be creative and engaging.
• The story should effectively convey the robot's emotional journey.

Code Generation

Difficulty: Hard

Prompt Challenge: Generate Python code to scrape data from a given website and store it in a CSV file. Include error handling and comments.

Starting Templates:

• Generate Python code to scrape data from [website URL] and store it in a CSV file.

• Write Python code to extract information from a website and save it to a CSV file.

• Create Python code to web scrape [website URL] and output the results to a CSV.

Solution Approaches:

• Use few-shot examples of web scraping code.
• Provide specific details about the website structure and data fields.
• Instruct the model to include error handling and comments.

Evaluation Criteria:

• The code should accurately scrape the data from the website.
• The code should store the data in a CSV file.
• The code should include error handling and comments.

AI-Powered Customer Service

A company implemented an LLM-powered chatbot to handle customer queries, leading to a 40% reduction in customer service costs and a 25% increase in customer satisfaction.

Automated Content Generation for Marketing

A marketing agency used LLMs to generate blog posts, social media content, and ad copy, resulting in a 50% increase in content output and a 30% reduction in content creation time.

AI-Assisted Code Development

A software development team used LLMs to generate code snippets, debug code, and create documentation, leading to a 35% increase in development speed and a 20% reduction in bug rates.

Clarity and Specificity

The principle of making prompts unambiguous and highly specific to guide the LLM towards the intended output.

Reduces ambiguity and ensures the LLM focuses on the desired information, leading to more accurate and relevant responses.

Practical Applications:

• Using precise language and avoiding vague terms.
• Defining the format of the output (e.g., list, paragraph, JSON).
• Specifying the persona or role the LLM should adopt.

Historical Context: Early research in NLP highlighted the importance of clear instructions for machine understanding, which translates to LLM prompting.

Contextual Awareness

The principle of providing relevant context within the prompt to help the LLM understand the situation and generate appropriate responses.

Ensures that the LLM is not operating in isolation, leading to more informed and contextually relevant outputs.

Practical Applications:

• Providing background information or relevant details.
• Referencing prior interactions or conversations.
• Establishing a clear scenario or situation.

Historical Context: The development of contextual embeddings in NLP enabled models to understand the relationships between words, a concept crucial for effective prompting.

Iterative Refinement

The process of incrementally improving prompts based on the LLM's responses, involving experimentation and adjustment.

Allows for continuous improvement of prompts, leading to more accurate and tailored outputs over time.

Practical Applications:

• Analyzing the LLM's output and identifying areas for improvement.
• Adjusting the prompt's wording, structure, or instructions.
• Experimenting with different prompting techniques.

Historical Context: The scientific method of observation, hypothesis, and experimentation applies to prompt engineering, highlighting its iterative nature.

Zero-shot Prompting

Asking the LLM to perform a task without providing any examples. Relies on the model's pre-existing knowledge.

Best Practices:

• Use clear and concise language.
• Specify the desired output format.
• Start with a simple prompt and iterate.

Common Pitfalls:

• Ambiguous or vague requests.
• Unrealistic expectations of the LLM's knowledge.
• Lack of context.

Example Prompts:

• Translate 'hello' to Spanish.

• Summarize the main points of the French Revolution.

• Write a short poem about the moon.

Use Cases:

• Simple translation.
• Basic text summarization.
• Creative writing tasks.

Few-shot Prompting

Providing a few examples of the desired input-output behavior to guide the LLM's response.

Best Practices:

• Use diverse examples covering different scenarios.
• Maintain a consistent pattern in the examples.
• Ensure the examples are clear and unambiguous.

Common Pitfalls:

• Using too few or irrelevant examples.
• Inconsistent formatting or style in the examples.
• Overly complex examples.

Example Prompts:

• Input: 'The cat sat on the mat', Output: 'cat-mat'. Input: 'The dog ran in the park', Output: 'dog-park'. Input: 'The bird flew over the tree', Output:

• Example 1: Question: 'What is the capital of France?', Answer: 'Paris'. Example 2: Question: 'What is the capital of Germany?', Answer: 'Berlin'. Question: 'What is the capital of Italy?'

Use Cases:

• Text classification.
• Data extraction.
• Pattern recognition.

Chain-of-Thought Prompting

Encouraging the LLM to explicitly reason through a series of intermediate steps before giving the final answer.

Best Practices:

• Use the phrase 'Let's think step by step' to encourage reasoning.
• Break down complex tasks into simpler steps.
• Guide the LLM with examples of the desired reasoning process.

Common Pitfalls:

• Overly complicated or unrealistic reasoning steps.
• Lack of clarity in the prompt's instructions.
• Expecting the LLM to perform complex calculations without proper guidance.

Example Prompts:

• A store has 10 apples and 5 oranges. If they sell 3 apples and 2 oranges, how many fruits are left? Let's think step by step.

• The meeting started at 2:00 PM and lasted for 1 hour and 30 minutes. What time did the meeting end? Let's think step by step.

Use Cases:

• Complex problem-solving.
• Logical reasoning tasks.
• Mathematical calculations.

Role Play Prompting

Template:

Act as a [role] and [task]. [Instructions]

Components Explanation:

• role: The persona or role the LLM should adopt (e.g., expert, teacher, journalist).
• task: The specific task the LLM should perform.
• instructions: Detailed instructions on how to perform the task.

When To Use:

• When you need the LLM to generate content with a specific style or perspective.
• When you need the LLM to adopt a particular role for a task (e.g. debugging code).
• When you need the LLM to act as a specific person or character.

Variations:

• Act as a [role] and [task] with the following constraints: [constraints].

• Act as a [role] and [task] with a [tone] tone.

• You are a [role]. [Task]. [Instructions].

Example Implementations:

• Act as a history professor and explain the causes of World War I.

• Act as a software engineer and debug the following code.

• You are a travel blogger. Write a review of a recent trip to Paris.

Fill-in-the-Blank Prompting

Template:

Complete the following: [context] ____ [task].

Components Explanation:

• context: The initial context or background information.
• task: The specific task the LLM should perform in the blank.

When To Use:

• When you need the LLM to generate specific words or phrases to complete a sentence or text.
• When you want the LLM to predict or infer the missing information.
• When you want the LLM to fill in gaps in a text or data.

Variations:

• Complete the following sentence: [context] ____

• Fill in the blank: [context] ____

• Given the context, what is missing here? [context] ____

Example Implementations:

• The capital of France is ____.

• The main character in Hamlet is ____.

• The formula for calculating area of circle is: area = pi * ____.

Question Answering Prompting

Template:

Answer the following question: [question]. [context].

Components Explanation:

• question: The specific question you want the LLM to answer.
• context: Relevant context or background information related to the question.

When To Use:

• When you need the LLM to answer specific questions based on provided context.
• When you want the LLM to extract specific information from a document.
• When you want the LLM to summarize a text and answer questions based on it.

Variations:

• Based on the following context, answer the question: [context] [question].

• Given the context, what is the answer to the following question? [context] [question].

• What is the answer to the following question: [question]. [context].

Example Implementations:

• Answer the following question: What is the capital of Spain? The capital of Spain is Madrid.

• Based on the following context, answer the question: What is the main topic of the article? The article discusses the rise of artificial intelligence. The main topic of the article is the rise of artificial intelligence.

• Given the context, what is the answer to the following question? [The quick brown fox jumps over the lazy dog] Which animal is jumping?

Generate a product description for an e-commerce website.

Problem: Create a compelling product description for a new wireless headphone, highlighting its key features and benefits.

Prompt Solution:

Write a product description for a wireless headphone. The product is named 'AuraSound'. Key features: noise cancellation, 20-hour battery life, comfortable earcups. Highlight the benefits of these features to the customer.

Output Analysis: The generated description should clearly list the key features and explain how these features benefit the customer. It should be persuasive and encourage purchases.

Optimization Tips:

• Experiment with different tones and styles (e.g., formal, casual).
• Include specific keywords that customers might search for.
• Add a call to action at the end of the description.

Extract information from a research paper.

Problem: Extract the key findings and methodologies from a research paper on climate change.

Prompt Solution:

Extract the key findings and methodologies from the following research paper: [paste research paper text]. Focus on the main results and the methods used to obtain them.

Output Analysis: The extracted information should include a summary of the main findings, a description of the methods used, and any significant results.

Optimization Tips:

• Specify the desired output format (e.g., bullet points, summary paragraph).
• Ask the LLM to provide the source of each extracted piece of information.
• Request specific types of information (e.g., statistical data, limitations of the study).

Generate a travel itinerary.

Problem: Create a 3-day travel itinerary for a trip to Rome, including key attractions and activities.

Prompt Solution:

Generate a 3-day travel itinerary for a trip to Rome. Include key attractions such as the Colosseum, Roman Forum, Pantheon, and Vatican City. Suggest activities and times for each day.

Output Analysis: The generated itinerary should include a balanced mix of key attractions and activities. It should be practical and feasible within a 3-day timeframe.

Optimization Tips:

• Specify the type of traveler (e.g., budget traveler, luxury traveler).
• Ask the LLM to include recommendations for restaurants and local experiences.
• Request the itinerary in a specific format (e.g., table, list).

GPT-4

Unique Features: Advanced reasoning, improved understanding of context, and better handling of complex tasks.

Optimal Prompting Strategies:

• Use detailed and specific instructions.
• Incorporate chain-of-thought prompting for complex problems.
• Experiment with few-shot prompting to provide examples of the desired output.

Limitations:

• Higher computational cost.
• May still produce inaccurate information despite advanced capabilities.
• Can be sensitive to subtle variations in prompts.

Best Practices:

• Start with simple prompts and gradually increase complexity.
• Use iterative refinement to improve the quality of the output.
• Test prompts with different variations to identify the most effective approach.

Bard

Unique Features: Strong focus on natural language understanding and creative text generation.

Optimal Prompting Strategies:

• Use natural and conversational language in prompts.
• Encourage creativity and imaginative responses.
• Leverage role-playing and storytelling prompts.

Limitations:

• May require more iterative refinement to achieve desired accuracy.
• Can sometimes produce overly creative responses.
• May not perform as well on tasks requiring strict logical reasoning.

Best Practices:

• Use clear and concise instructions.
• Provide examples of the desired tone and style.
• Guide the model with specific constraints.

Claude

Unique Features: Excellent at following complex instructions and producing structured output.

Optimal Prompting Strategies:

• Provide detailed and structured prompts.
• Use clear examples of the desired output format.
• Leverage its ability to handle multi-turn conversations effectively.

Limitations:

• May not perform as well on tasks requiring creativity or imagination.
• Can be sensitive to subtle variations in prompt structure.
• May require more explicit instructions for nuanced tasks.

Best Practices:

• Use specific keywords to guide the model.
• Break down complex tasks into smaller sub-tasks.
• Clearly define the desired output format and structure.

Text Generation

Difficulty: Beginner

Prompt Challenge: Generate a short story about a robot who wants to be a chef.

Starting Templates:

• Write a short story about a robot named [robot's name] who dreams of becoming a chef.

• Compose a narrative about a robot who learns to cook.

• Write a story about a robot chef.

Solution Approaches:

• Use role-playing prompting to guide the LLM.
• Provide examples of similar stories to inspire the LLM.
• Experiment with different tones and styles.

Evaluation Criteria:

• Creativity and originality of the story.
• Clarity and coherence of the narrative.
• Engagement and entertainment value of the story.

Data Extraction

Difficulty: Intermediate

Prompt Challenge: Extract all the names and email addresses from a given text document.

Starting Templates:

• Extract all names and email addresses from the following text: [paste text].

• Identify and list all names and email addresses present in the document below: [paste text].

• From the following text, extract all names and email addresses: [paste text].

Solution Approaches:

• Use specific keywords to guide the LLM.
• Provide examples of names and email addresses.
• Specify the desired output format (e.g., list, table).

Evaluation Criteria:

• Accuracy of the extracted information.
• Completeness of the extraction (all names and email addresses should be found).
• Correct formatting of the extracted data.

Problem Solving

Difficulty: Advanced

Prompt Challenge: Develop a chain-of-thought prompt to solve a complex mathematical word problem.

Starting Templates:

• Solve the following mathematical word problem using chain-of-thought prompting: [paste problem].

• Use chain-of-thought prompting to break down and solve the following problem: [paste problem].

• Apply chain-of-thought to solve this math problem: [paste problem].

Solution Approaches:

• Break down the problem into smaller steps.
• Use the phrase 'Let's think step by step' to guide the LLM.
• Provide examples of similar reasoning processes.

Evaluation Criteria:

• Accuracy of the final answer.
• Clarity and logic of the reasoning process.
• Correct application of chain-of-thought prompting.