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Prompt Engineering 101: Boosting LLM Output

Lucas P Chatham -

Intro

AI is here to stay. So is prompt engineering. This guide is your deep dive into getting the most out of Large Language Models.

Warning: This blog is long. It's comprehensive. It's meant to be. If you want a quick starter cheat sheet on how to prompt, skip to What's the anatomy of a prompt? section. If you want more nuanced methods to improve your prompts, skip to the Prompting Principles section.

The goal of this post was to train my neurons on how to more effectively prompt engineer- hope it does the same for you.

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English is the best programming language of the future." Jensen Huang, NVIDIA CEO

Background

AI vs ML vs GenAI

To understand why prompt engineering matters, you need to first understand Generative AI (GenAI). Artificial Intelligence (AI) is the broad field focused on enabling machines to perform tasks that require human-like intelligence. Machine Learning (ML) is a subset of AI that uses algorithms to learn from data and make predictions, while Generative AI (GenAI) is a specialized branch of ML that creates new content, such as text and images, by mimicking patterns from existing data. Together, these technologies enhance each other, with ML improving GenAI's training and GenAI generating synthetic data for ML models.

Aspect AI Machine Learning (ML) Generative AI (GenAI)
Function Performs human-like tasks Analyzes data to make predictions Generates new content
Algorithm Type Various types Data pattern recognition Advanced, creative algorithms
Output Decisions, classifications Predictions, classifications Text, images, audio, video
Applications Broad range Data analysis, cybersecurity Content creation, design

Why is prompt engineering important?

Prompt engineering is crucial because it serves as the bridge between human intent and machine output, ensuring that AI models accurately understand and respond to queries. By crafting precise and context-rich prompts, users can significantly enhance the relevance and quality of AI-generated responses, leading to improved decision-making and user satisfaction. Additionally, effective prompt engineering minimizes the need for extensive revisions by guiding AI systems to produce desired outcomes efficiently, ultimately unlocking the full potential of generative AI technologies.

What's the anatomy of a prompt?

Here's the anatomy of a prompt. Try using this anatomy as a checklist for any prompt you write for which you'd like to maximize the quality of the output.

  1. Task context: Assign the LLM a role or persona and broadly define the task it is expected to perform.
  2. Tone context: Set a tone for the conversation in this section.
  3. Background data (documents and images): Also known as context. Use this section to provide all the necessary information for the LLM to complete its task.
  4. Detailed task description and rules: Provide detailed rules about the LLM’s interaction with its users.
  5. Examples: Provide examples of the task resolution for the LLM to learn from them.
  6. Conversation history: Provide any past interactions between the user and the LLM, if any.
  7. Immediate task description or request: Describe the specific task to fulfill within the LLM's assigned roles and tasks.
  8. Think step-by-step: If necessary, ask the LLM to take some time to think or think step by step.
  9. Output formatting: Provide any details about the format of the output.
  10. Prefilled response: If necessary, pre-fill the LLM's response to make it more succinct.

What are the first principles of prompt engineering?

Prompt engineering is grounded in several first principles that guide effective interaction with language models. These principles emphasize maximizing relevant information while minimizing noise, ensuring task clarity, and leveraging the model's capabilities through tailored prompts that reflect user intent and context. By understanding these foundational concepts, you can refine your approach to prompt design; expand the below to peek more deeply into the first principles of prompt engineering.

First Principles of Prompt Engineering

  1. Information Theory and Context
    Principle: Maximize relevant information while minimizing noise.
    Application: Craft prompts that provide essential context without overwhelming the model with irrelevant details.
  2. Model Interaction
    Principle: The prompt is a form of communication with the LLM's learned representations.
    Application: Design prompts that effectively "speak the language" of the model, leveraging its training and capabilities.
  3. Task Clarity
    Principle: The model can only perform tasks it understands.
    Application: Provide clear, unambiguous instructions that define the task and desired outcome.
  4. Constrained Creativity
    Principle: LLMs generate probabilistic outputs based on learned patterns.
    Application: Use constraints in prompts to guide the model's creative generation within desired boundaries.
  5. Context Window Utilization
    Principle: The model's performance is limited by its context window.
    Application: Efficiently use the available token space to provide necessary information and instructions.
  6. Iterative Refinement and Feedback
    Principle: Prompt engineering is an empirical process that benefits from continuous improvement.
    Application: Continuously test, refine, and adapt prompts based on model outputs, task requirements, and user feedback.
  7. Behavioral Priming
    Principle: The model's behavior can be influenced by the framing of the task.
    Application: Use role-playing, tone setting, and explicit behavioral instructions to guide model outputs.
  8. Cognitive Emulation
    Principle: LLMs can emulate cognitive processes when properly prompted.
    Application: Design prompts that guide the model through human-like reasoning steps (e.g., chain-of-thought prompting).
  9. Ethical Consideration
    Principle: LLMs can produce biased or inappropriate content based on their training data.
    Application: Incorporate ethical guidelines and constraints into prompts to promote responsible AI use.
  10. User Intent Alignment
    Principle: Understanding the user's intent is crucial for effective communication with the model.
    Application: Analyze the user's goals and tailor prompts to align closely with their specific needs, ensuring that the model's outputs are relevant and useful.
  11. Multimodal Integration
    Principle: Many applications may involve multiple forms of data (text, images, etc.).
    Application: When applicable, consider how different modalities can be integrated into prompts to enrich the context and improve the model's understanding and output.
  12. Scalability and Modularity
    Principle: Effective prompts should be adaptable across different tasks or contexts.
    Application: Create templates or modular prompts that can be easily adapted for various scenarios, ensuring efficiency in prompt generation while maintaining effectiveness.
  13. Model Limitation Awareness
    Principle: Recognizing the limitations of LLMs helps in crafting more effective prompts.
    Application: Design prompts that account for potential weaknesses or biases in the model, such as ambiguity in language, lack of domain-specific knowledge, or outdated information.
  14. Emotional Intelligence
    Principle: The emotional tone of prompts can influence the model's responses.
    Application: Use emotionally intelligent language in prompts to foster appropriate responses, especially in sensitive contexts or when user engagement is a priority.
  15. Contextual Relevance Over Time
    Principle: Context can evolve during a conversation or interaction.
    Application: Structure prompts to maintain coherence over extended interactions, allowing for context updates that reflect changes in user needs or conversation flow.
  16. Prompt Efficiency
    Principle: Concise prompts can often be as effective as longer ones.
    Application: Strive for brevity without sacrificing clarity, optimizing for both token usage and effectiveness.
  17. Domain Specificity
    Principle: Different domains may require tailored prompting strategies.
    Application: Adapt prompting techniques to the specific field or topic, considering domain-specific terminology and reasoning patterns.
  18. Error Handling and Verification
    Principle: LLMs can make mistakes or produce inconsistent outputs.
    Application: Incorporate error-checking mechanisms in prompts, such as asking the model to verify its own outputs or provide confidence levels.
  19. Prompt Chaining
    Principle: Complex tasks can be broken down into a series of simpler prompts.
    Application: Design sequences of prompts that build upon each other to tackle more complex problems.
  20. Data Awareness
    Principle: LLMs' knowledge and capabilities are fundamentally shaped by their training data.
    Application: Design prompts with an understanding of the model's training data limitations, including knowledge cutoff dates, potential biases, and domain coverage.
    1. Temporal awareness: Recognizing that the model's knowledge has a cutoff date and may not include recent events or developments.
    2. Domain coverage: Understanding which areas the model is likely to have strong or weak knowledge in based on its training data.
    3. Bias recognition: Being aware that biases present in the training data may be reflected in the model's outputs.
    4. Data quality consideration: Recognizing that the quality and reliability of the model's knowledge can vary based on the quality of its training data.
    5. Multilingual and cultural aspects: Understanding how the model's training data affects its performance across different languages and cultures.

What important assumptions should be considered in prompt engineering?

  1. Clear Definition of Success Criteria
    1. Specific: Define clear metrics, e.g., "accurate sentiment classification" instead of vague terms like "good performance."
    2. Measurable: Use quantifiable metrics, e.g., "less than 0.1% of outputs flagged for toxicity in 10,000 trials" instead of "safe outputs."
    3. Achievable: Set realistic targets based on industry benchmarks, prior experiments, or expert knowledge.
    4. Relevant: Ensure criteria align with the application's purpose and user needs.
  2. Way to Empirically Test Against Those Criteria
    1. Be Task-Specific: Design evaluations that reflect real-world task distributions, including edge cases.
    2. Automate When Possible: Streamline testing processes to improve efficiency.
    3. Prioritize Volume Over Quality: Focus on more questions with slightly lower signal rather than fewer high-quality human-graded evaluations.
    4. Have Detailed, Clear Rubrics: Specify requirements clearly, e.g., "The answer should always mention 'Acme, Inc.' in the first sentence."
    5. Empirical or Specific is Best: Instruct the LLM to provide binary outputs ("correct" or "incorrect") or a rating scale (1-5) for faster assessment.
    6. Encourage Reasoning: Ask the LLM to think through its evaluation before providing a score, which enhances performance for complex judgments.

Prompt Engineering Principles

These prompt engineering principles outline essential principles that ensure clarity, specificity, and relevance in your prompts. By mastering these principles, you can transform vague instructions into precise commands that drive optimal responses from AI. Each principle serves as a building block for effective communication, enabling you to articulate your needs clearly and efficiently. Whether you're crafting a simple query or a complex task, these strategies will help you harness the full potential of AI technology.

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Golden rule of prompting: if you show a colleague your prompt, ideally someone with minimal context on the task, and they're confused, it's likely your LLM will be, too.


I. General Prompting Techniques

  1. Priming the Chatbot
    • Use the initial prompt to set the conversation's structure, style, and context.
    • Example: "You are a helpful assistant that provides concise answers in a friendly tone."
  2. Controlling AI Output Structure
    • Provide explicit instructions to shape the format of the AI's responses.
    • Example: "Please list the features in bullet points."
  3. Zero-Shot, One-Shot, and Few-Shot Prompting
    • Zero-Shot Prompting
      • No examples provided; relies on pre-trained knowledge.
      • Advantages: Quick and simple.
      • Disadvantages: May lack precision for complex tasks.
    • Single-Shot Prompting
      • Provide one example as a reference.
      • Advantages: More guidance than zero-shot.
      • Disadvantages: Limited context.
    • Few-Shot Prompting
      • Provide multiple examples (3-5) to guide the model.
      • Benefits:
        • Improves accuracy and consistency.
        • Reduces misinterpretation.
        • Enhances performance on complex tasks.
      • Tips:
        • Use relevant and diverse examples.
        • Format examples clearly (e.g., <example> tags).
        • Encourage the AI to generate additional examples.
  4. Chain-of-Thought (CoT) Prompting
    • Encourage step-by-step reasoning for complex tasks. See example below. Always have the AI display its thought process.
    • Approaches:
      • Basic CoT
        • Prompt: "Think step-by-step: If a train travels 120 miles in 2 hours, what is its average speed?"
      • Guided CoT
        • Provide specific steps to follow.
        • Prompt: "Identify the given information, determine the formula, calculate the result, and state the final answer."
      • Iterative CoT
        • Break down problems into sub-problems and solve them sequentially.
      • Structured CoT
        • Break down the problem into clearly defined steps, ensuring each step builds on the previous one to arrive at the final answer.
        • Use tags like <thinking> for reasoning and <answer> for the final result.
  5. Self-Consistency
    • Ask the model the same prompt multiple times and take the majority result.
    • Example: Solve a problem several times and choose the most frequent answer to increase confidence.
  6. Tree-of-Thought (ToT) Prompting
    • Break down complex problems into a tree of possible reasoning paths.
    • Explore multiple reasoning paths before arriving at the final answer.
    • Example: "Consider all possible approaches to solve the problem and choose the best solution."
  7. Organize Prompts Using XML or Structured Formats
    • Structure prompts and responses with XML or JSON tags for clarity.
    • Best Practices:
      • Use consistent, meaningful tag names (e.g., <instruction>, <context>).
      • Nest tags appropriately.
      • Reference content by tag names.
      • Ensure all tags are properly closed.
  8. Prompt Structure and Clarity
    • Specify the intended audience (e.g., "Explain to an expert in the field").
    • Use affirmative directives like "Do" instead of "Don't."
    • Use leading phrases like "Think step by step."
    • End your prompt with the beginning of the desired response.
    • Use delimiters or headings to separate sections (e.g., ### Instruction ###).
  9. Specificity and Information
    • Clearly state requirements and expectations.
    • Encourage simple explanations (e.g., "Explain like I'm 5 years old").
    • Instruct the AI to avoid biases and stereotypes.
    • Provide samples or starting points to guide style and content.
  10. User Interaction and Engagement
    • Allow the AI to ask clarifying questions.
    • Encourage detailed and thorough responses.
    • Example: "From now on, ask me questions to gather more information before answering."
  11. Content and Language Style
    • Instruct the AI to improve text without changing the original style.
    • Assign a role or persona to set tone and vocabulary.
    • Use phrases like "Your task is" and "You must."
    • Be direct; unnecessary politeness is not needed.
    • Emphasize key points by repeating important words.
  12. Handling Complex Tasks and Coding Prompts
    • Break down complex tasks into simpler steps.
    • For multi-file coding tasks, instruct the AI to generate scripts that automate file creation.
    • Combine Chain-of-Thought with few-shot prompting for better results.
  13. Utilize Model Parameters (API Usage)
    • Use parameters like 'role' to assign a permanent context or persona to the model.
  14. Encourage the Model to Think
    • Include phrases like "Think step by step" to promote thorough reasoning.
    • Use tags (e.g., <thinking>) to capture the thought process.
  15. Break Complex Tasks into Subtasks (Prompt Chaining)
    • Use prompt chaining by feeding the output of one prompt into the next.
    • Improves focus and accuracy on each subtask.
    • Example: "First, summarize the text. Then, extract key themes from the summary."
  16. Leverage Long Context Windows
    • Utilize the model's ability to handle extensive information effectively.
    • Structure prompts to make the best use of the AI's context capacity.
  17. Allow the Model to Express Uncertainty
    • Encourage the AI to say "I don't know" if unsure.
    • Instruction: "If you are unsure, say 'I don't know.'"
  18. Role-Playing and Persona Assignment
    • Assign the AI a specific role to influence its responses.
    • Example: "You are a historian specializing in ancient Egypt."
  19. Iterative Refinement
    • Ask the AI to refine its previous response based on new information or feedback.
    • Example: "Based on the feedback provided, please revise your answer."
  20. Instruction Following
    • Use clear and direct instructions to guide the AI.
    • Example: "Summarize the following text in two sentences."
  21. Self-Evaluation and Correction
    • Instruct the AI to check and correct its own output.
    • Example: "Provide the answer and then verify its correctness before presenting it."
  22. Context Preservation
    • Provide context explicitly to maintain coherence in the conversation.
    • Example: "Given the previous discussion about renewable energy..."
  23. Avoiding Ambiguity
    • Phrase prompts to minimize misunderstanding.
    • Example: "Explain the process step by step, focusing on the chemical reactions involved."
  24. Limiting Responses
    • Set constraints on the length or format of the response.
    • Example: "Provide your answer in no more than 200 words."
  25. Using Analogies and Metaphors
    • Encourage the use of analogies to explain complex ideas.
    • Example: "Explain quantum mechanics using a simple analogy."
  26. Highlighting Key Points
    • Ask the AI to emphasize important information.
    • Example: "Summarize the key takeaways from the report."
  27. Emotion and Tone Control
    • Specify the emotional tone or style of the response.
    • Example: "Respond in a compassionate and understanding tone."
  28. Prompting for Multiple Perspectives
    • Instruct the AI to consider different viewpoints.
    • Example: "Discuss the advantages and disadvantages of remote work."
  29. Safety and Ethical Guidelines
    • Remind the AI to adhere to ethical considerations.
    • Example: "Ensure your response is appropriate and avoids sensitive content."
  30. Utilizing Prompt Templates
    • Use standardized templates for common tasks.
    • Example: "Use the following format for your report: Introduction, Methods, Results, Conclusion."
  31. Help the AI Learn by Example
    • Provide examples to enhance the model's understanding and performance.
    • Example: "Here's how to solve similar problems: [Provide examples]. Now solve this new problem."
  32. Maintaining Desired Response Format
    • Instruct the AI to follow specific output formats (e.g., JSON, XML).
    • Example: "Provide the output in JSON format with the following keys..."
  33. Pre-filling the AI's Response
    • Begin the assistant's response with a predefined format or text.
    • Example: "Answer starts here:"
  34. Give the AI Time to Think
    • Encourage the AI to take time for reasoning before answering.
    • Instruction: "Take a moment to think through the problem before responding."
  35. Avoiding Open-Ended Prompts
    • Be specific to guide the AI effectively.
    • Example: Instead of "Tell me about trees," ask "Explain the process of photosynthesis in trees."
  36. Repetition for Emphasis
    • Repeat key instructions to ensure they are followed.
    • Example: "Remember, do not include any personal opinions. Do not include any personal opinions.

II. Techniques Specific to Image/Video Generation AI

  1. Image Prompt Techniques
    • Placement and Size
      • Place images at the beginning of the prompt.
      • Resize images to balance clarity and file size.
    • Applying Text Prompt Techniques
      • Use traditional methods (e.g., defining a role) with image inputs.
    • Using Images as Examples
      • Provide multiple images for reference.
      • Use image tags to identify and reference images clearly.
  2. Working with Complex Graphics
    • Ask the AI to describe each data point in detailed charts.
    • Have the AI identify color codes to distinguish similar colors.
    • Example: "Describe the data represented in the chart, including all color codes."
  3. Narrating Slide Decks
    • Convert slides to images, one per slide.
    • Instruct the AI to narrate each slide for comprehensive understanding.
    • Example: "Provide a detailed narration of each slide image provided."
  4. Style and Artistic Direction
    • Specify the artistic style or genre for image generation.
    • Example: "Generate an image of a sunset in the style of Van Gogh."
  5. Resolution and Format Specifications
    • Define the desired resolution and file format.
    • Example: "Create a 1920x1080 PNG image of a futuristic cityscape."
  6. Content Restrictions
    • Set boundaries to avoid inappropriate or undesired content.
    • Example: "Generate an image of a cat, avoiding any violent or graphic elements."
  7. Using Seed Images
    • Provide reference images to guide the generation process.
    • Example: "Based on this image [insert image], create a night-time version."
  8. Leveraging Visual Prompts
    • Add prompts directly within images to guide the AI.
    • Ensure embedded text is clear and legible.
  9. Adjusting Model Settings for Image Analysis
    • Set parameters like 'temperature' to control response randomness when analyzing images.
  10. Detailed Descriptions for Complex Images
    • Provide thorough descriptions when working with intricate visuals.
    • Example: "Describe the intricate patterns and their significance in the provided image."

Frequently Asked Questions

Frequently Asked Questions (FAQ) on Prompting Principles for Large Language Models (LLMs)1. What is priming a chatbot, and why is it important?

  • Priming involves setting the initial context, role, or style of the AI assistant in your first prompt.
  • By defining these parameters upfront, you guide the AI's responses to align with specific requirements, controlling aspects like tone, formality, and focus.
  • This ensures the interaction meets your intended purpose and yields more relevant results.

2. What are zero-shot, one-shot, and few-shot prompting?

  • Zero-Shot Prompting:
    • The model receives only the task description without any examples, relying entirely on its pre-trained knowledge.
    • While quick, it may yield less accurate results for complex tasks.
  • One-Shot Prompting:
    • One example is provided along with the task description.
    • This guides the model toward the desired output but may still be insufficient for nuanced tasks.
  • Few-Shot Prompting:
    • Several examples (typically 3-5) are given with the task description.
    • These help the model understand the requirements, significantly improving performance on tasks needing specific outputs.

3. How does Chain-of-Thought (CoT) prompting work?

  • CoT prompting instructs the AI to generate intermediate reasoning steps leading to the final answer.
  • By encouraging the model to "think aloud," it produces a logical sequence, enhancing accuracy on complex tasks like problem-solving and multi-step analyses.

4. What is self-consistency in prompting?

  • Self-consistency involves sampling multiple reasoning paths and selecting the most consistent conclusion.
  • By generating several Chain-of-Thought sequences and aggregating the answers, it reduces variability and increases the reliability of the AI's response, especially in complex problem-solving.

5. How can I use structured formats like XML in prompts?

  • Using structured formats (e.g., XML, JSON) allows you to explicitly delineate sections like instructions, context, and input data.
  • This reduces ambiguity and helps the model parse the prompt effectively.
  • Consistent and meaningful tags (like <instruction>, <context>) improve response quality, especially for complex tasks.

6. Why should I assign a role or persona to the AI?

  • Assigning a role or persona guides the AI to generate responses aligned with specific expertise, tone, or style.
  • For example, specifying "You are a cybersecurity expert" influences the model to use relevant knowledge and terminology.
  • This results in more accurate outputs for specialized tasks.

7. How can I reduce hallucinations or inaccuracies in AI outputs?

  • Provide clear and specific prompts, and limit open-ended questions.
  • Encourage the AI to admit uncertainty by including instructions like "If you're unsure, please say 'I don't know.'"
  • Requesting evidence or sources can also improve accuracy.

8. What are some general tips for crafting effective prompts?

  • Be clear and specific in your instructions.
  • Use affirmative language rather than negatives.
  • Provide examples when necessary.
  • Break complex tasks into smaller steps.
  • Specify the desired output format or style explicitly.

9. How do I get the AI to produce outputs in a specific format?

  • Include explicit instructions detailing the desired structure.
  • For example, "Provide the results in JSON format with the fields 'name', 'age', and 'occupation.'"
  • Supplying a template or sample output can further guide the model.

10. What are some limitations of LLMs I should be aware of?

  • Bias and Fairness:
    • LLMs may reflect biases present in their training data, potentially reinforcing stereotypes or unfair assumptions.
  • Hallucinations:
    • The AI might generate plausible but incorrect information, especially when uncertain.
  • Mathematical Limitations:
    • LLMs can struggle with complex arithmetic and mathematical reasoning.
  • Lack of Source Attribution:
    • The AI typically cannot cite sources or verify the origin of information.
  • Vulnerability to Prompt Injection:
    • Malicious prompts can manipulate the AI into producing unintended or harmful outputs.
  • Context Length Limitations:
    • The model has a finite context window, affecting performance on tasks requiring long documents.

11. What is the difference between soft prompts and hard prompts?

  • Hard Prompts:
    • Human-readable text inputs provided at inference time.
    • Consist of explicit instructions or questions that guide the model without modifying its internal parameters.
  • Soft Prompts:
    • Continuous embeddings learned during prompt tuning.
    • Influence the model's behavior when prepended to the input and are not interpretable by humans.

12. How can I encourage the AI to think step-by-step?

  • Include instructions like "Explain your reasoning step-by-step before giving the final answer."
  • This leverages Chain-of-Thought prompting, enhancing the model's ability to perform complex reasoning.
  • Making its thought process explicit improves transparency.

13. What is Tree-of-Thought (ToT) prompting?

  • ToT prompting is an advanced technique where the model explores multiple reasoning paths like branches of a tree.
  • By considering various approaches to solving a problem, the AI can evaluate different solutions.
  • This is useful for complex decision-making and problem-solving.

14. How can I use examples effectively in my prompts?

  • Provide several clear and relevant examples demonstrating the desired task.
  • Consistent formatting helps the model recognize patterns.
  • Include input-output pairs that illustrate how the model should process similar requests.

15. How do prompting techniques differ for image or video generation AI?

  • For image or video generation, prompts should include detailed descriptions of the desired visual output.
    • Specify content, style, and composition.
  • Parameters like resolution and format guide the model.
  • When analyzing images, place them at the beginning of the prompt and provide textual context.

16. What are best practices when working with images in prompts?

  • Place images at the start of the prompt.
  • Resize images to balance clarity and file size.
  • Use image tags (e.g., <image1>) for clear reference.
  • Provide detailed descriptions for complex images.
  • Apply text prompting techniques to guide analysis.

17. How does adding emotion or tone into prompts influence AI outputs?

  • Specifying the desired emotional tone or style influences the AI's language and presentation.
  • Instructions like "Respond in a formal, academic tone" or "Explain in a friendly manner" guide the AI to adjust responses.
  • This enhances relevance and effectiveness.

18. How do I handle complex tasks or multi-step problems in prompts?

  • Break down the task into sequential subtasks.
  • Use prompt chaining by feeding the output of one prompt into the next.
  • This allows the AI to focus on one aspect at a time, improving accuracy.

19. What is prompt chaining, and how does it help with complex tasks?

  • Prompt chaining uses the AI's output from one prompt as input for the next.
  • It guides the AI through a series of steps.
  • This technique divides complex tasks into smaller parts, improving focus and accuracy.

20. How do I instruct the AI to express uncertainty when it doesn't know an answer?

  • Include instructions like "If you're unsure or lack sufficient information, please say 'I don't know.'"
  • This encourages the AI to admit uncertainty rather than providing incorrect information.

21. How can I control the style and language of the AI's responses?

  • Provide clear instructions about the desired style, tone, or vocabulary.
  • For example, "Use technical language appropriate for software engineers" or "Explain in simple terms suitable for beginners."

22. Why is specificity important in prompts?

  • Specific prompts reduce ambiguity, helping the AI understand exactly what you want.
  • Clear instructions lead to more accurate responses, minimizing misunderstandings.

23. How can I ensure that the AI avoids bias and stereotypes in its responses?

  • Include instructions like "Ensure your answer is unbiased and free from stereotypes."
  • Review outputs critically, as the AI may still reflect biases from its training data.

24. How does the LLM's understanding of context affect its responses?

  • LLMs rely on the context provided in the prompt and previous interactions.
  • Clear and explicit context ensures the AI generates coherent and relevant responses.

25. How do I get the AI to ask me questions to clarify my request?

  • Encourage interaction by including instructions like "Feel free to ask any questions to better understand the task."
  • This prompts the AI to seek clarification, leading to more accurate responses.

26. How can I adjust the length or depth of the AI's responses?

  • Specify your expectations in the prompt.
  • For example, "Provide a brief summary in two sentences" or "Write a detailed analysis covering all key aspects."
  • This guides the AI to meet your requirements.

27. What is the importance of using delimiters or headings in prompts?

  • Using delimiters like ### Instruction ### organizes the prompt.
  • It makes it easier for the AI to parse and understand different sections.
  • This enhances clarity and improves response quality.

28. How can I use self-evaluation and correction in AI outputs?

  • Instruct the AI to review and correct its response before presenting it.
  • For example, "Provide your answer, then check for errors before finalizing."
  • This encourages self-checking, reducing mistakes.

29. How does the temperature setting affect the AI's responses?

  • The temperature controls randomness in the model's output.
  • Low Temperature (e.g., 0.2):
    • Produces more deterministic and focused responses, suitable for tasks requiring precision.
  • High Temperature (e.g., 0.8):
    • Introduces variability, useful for creative tasks like storytelling.

30. What are known biases or limitations in LLMs, and how can I mitigate them?

  • Biases:
    • The AI may reflect societal biases from training data.
    • Mitigate by crafting unbiased prompts and reviewing outputs critically.
  • Reasoning Limitations:
    • The AI might struggle with common-sense reasoning or knowledge gaps.
  • Mitigation Strategies:
    • Provide clear instructions and necessary context.
    • Validate outputs, especially for critical applications.

31. How can I get the AI to output in a specific format like JSON or Markdown?

  • Clearly state the desired format in your prompt and provide an example if possible.
  • For instance, "Please provide the output in JSON format as follows..."
  • This guides the AI to produce easily parsed outputs.

32. Why is it unnecessary to use polite language like 'please' or 'thank you' in prompts?

  • While not harmful, polite language isn't necessary.
  • Being direct and clear is more effective in conveying instructions.
  • This ensures better adherence to the task.

33. How can I use repetition for emphasis in prompts?

  • Repeating key instructions can reinforce them.
  • For example, "Do not include personal opinions. Do not include personal opinions."
  • This reduces the chance of the AI overlooking important directives.

34. What is the benefit of the AI displaying its thought process?

  • Displaying the AI's reasoning helps you understand how it arrived at an answer.
  • It ensures transparency.
  • It aids in verifying accuracy and identifying errors in complex responses.

35. How can I use the AI to improve or revise text without changing its original style?

  • Instruct the AI to focus on specific aspects like grammar while maintaining the original tone.
  • For example, "Improve the following paragraph for grammar without altering its style."

36. How does specifying the intended audience affect the AI's response?

  • Indicating the audience helps the AI tailor language and complexity appropriately.
  • For example, "Explain this concept to a high school student" prompts simpler language and foundational explanations.

37. What are ways to encourage the AI to focus on important information?

  • Ask the AI to highlight key points or summarize main ideas.
  • For example, "List the three most critical factors affecting climate change."

38. How can I use analogies or metaphors in prompts to enhance explanations?

  • Instruct the AI to use analogies to simplify complex ideas.
  • For example, "Explain blockchain technology using a simple analogy suitable for beginners."

39. Why is it helpful to prefill the AI's response or provide the beginning of the answer?

  • Starting the response for the AI guides it on how to continue.
  • It ensures alignment with your expectations in style and content.
  • It sets the tone for the rest of the output.

40. How can I leverage the AI's ability to process long context windows?

  • Provide all relevant information within the prompt, structured effectively.
  • This allows the AI to reference earlier parts, enhancing coherence and continuity.

41. Why should I avoid open-ended prompts?

  • Open-ended prompts can lead to ambiguous or irrelevant responses.
  • Being specific helps the AI focus on the desired topic, generating more accurate answers.

42. How important is context preservation in AI interactions?

  • Consistent context is crucial for maintaining coherence.
  • Including relevant information ensures the AI generates appropriate responses, especially in extended conversations.

43. How can I limit the AI's responses in terms of length or content?

  • Specify constraints in your prompt.
  • For example, "Limit your response to 150 words" or "Provide only the key points."
  • This controls the length and focus of the AI's output.

44. How can I ensure the AI adheres to safety and ethical guidelines?

  • Include instructions reminding the AI to avoid inappropriate content and adhere to ethical standards.
  • For example, "Ensure your response is appropriate for all audiences and does not include offensive material."

45. Can I ask the AI to refine its previous response based on feedback?

  • Yes, you can instruct the AI to revise or improve upon its earlier output.
  • For example, "Based on the feedback provided, please revise your previous answer to address the following points..."

Resources

  1. Learn by doing with Anthropic’s Claude 3 on Amazon Bedrock | AWS Machine Learning Blog
  2. Prompt Engineering Overview - Anthropic
  3. Create Strong Empirical Evaluations - Anthropic
  4. Prompt Engineering Principles for 2024
  5. [2312.16171v1] Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4
  6. [2305.13252] "According to ...": Prompting Language Models Improves Quoting from Pre-Training Data
  7. [2201.11903] Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
  8. [2402.07927] A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications
  9. OpenAI Prompt Engineering Guide
  10. Prompt Engineering: Using Intelligence to Use Artificial… | by Research Graph | Medium
  11. Pecan.ai: Generative AI vs Machine Learning - Comparing
  12. Seldon.io: Generative AI vs Machine Learning
  13. Revelo.com: Generative AI vs Machine Learning
  14. Forbes: The Vital Difference Between Machine Learning and Generative AI
  15. Rackspace: Distinctions Between AI, ML, and Generative AI
  16. Oracle: Understand the Differences Between AI, GenAI, and ML
  17. Blue Prism: Generative AI vs Machine Learning
  18. OurCrowd: Machine Learning vs Generative AI
  19. Best Practices for Prompt Engineering with the OpenAI API
  20. Google Cloud Blog: Best Practices for Prompt Engineering
  21. Prompt Engineering Guide
  22. Best Practices for Prompt Engineering - Reddit Discussion
  23. https://www.hostinger.com/tutorials/ai-prompt-engineering
  24. https://www.techtarget.com/searchenterpriseai/tip/Prompt-engineering-tips-and-best-practices
  25. https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-the-openai-api
  26. https://cloud.google.com/blog/products/application-development/five-best-practices-for-prompt-engineering?hl=en
  27. https://www.promptingguide.ai
  28. https://www.reddit.com/r/PromptEngineering/comments/1dunfih/best_practices_for_prompt_engineering/
  29. https://platform.openai.com/docs/guides/prompt-engineering
  30. https://www.freecodecamp.org/news/how-to-make-a-dynamic-table-of-contents-in-javascript/