Learn With Nathan
  • AI Chat Tools
    • ChatGPT - OpenAI
      • Start with ChatGPT
      • Account Settings
      • ChatGPT Free Plan
      • ChatGPT Account Settings
    • Claude - Anthropic
      • Signup for Claude
      • User Interface
    • Gemini - Google
  • AI Concepts
    • Context
    • Tokenization
    • Prompt Engineering
    • Temperature
    • Max Tokens
    • Fine-Tuning
    • System Prompt
    • Persona
    • Memory
    • Hallucination
    • Model Bias
    • Embedding
    • Latency
    • User Intent
    • Multimodal AI
    • Safety Layers
    • Chain of Thought
    • Prompt Templates
    • Retrieval-Augmented Generation (RAG)
  • Introduction to Prompting
    • Beginner's Prompting Strategies
      • Understanding the Purpose of a Prompt
      • Be Specific and Clear
      • Using Contextual Information
      • Direct vs. Open-Ended Prompts
      • Step-by-Step Instructions
      • Role-Based Prompts
      • Sequential Prompts
      • Multi-Step Questions
      • Incorporating Examples
    • Common Prompting Mistakes to Avoid
      • Being Too Vague or Ambiguous
      • Overloading with Multiple Questions
      • Ignoring Context Limitations
      • Not Specifying the Desired Output
      • Lack of Iteration and Refinement
      • Neglecting to Set the Right Tone or Role
      • Using Jargon or Complex Language Unnecessarily
      • Ignoring Feedback from the AI
      • Overly Long or Short Prompts
      • Page 6
      • Page 5
      • Page 4
      • Page 3
      • Page 2
      • Page 1
    • Output Formatting Techniques
      • Using Headings and Subheadings
      • Bulleted and Numbered Lists
      • Paragraph Structure
      • Tables and Charts
      • Direct Answers vs. Detailed Explanations
      • Incorporating Summaries and Conclusions
    • Leveraging Formatting for Clarity
      • Highlighting Key Points
      • Guiding the AI on Tone and Style
      • Requesting Examples or Case Studies
      • Formatting for Different Audiences
      • Using Questions to Clarify Information
      • Prompting for Step-by-Step Guides
      • Customizing Responses for Presentation or Reports
      • Avoiding Over-Complicated Formatting
  • Types of Prompts
    • Direct Prompts
    • Instructional Prompts
    • Conversational Prompts
    • Contextual Prompts
    • Example-Based Prompts
    • Reflective or Feedback Prompts
    • Multi-Step Prompts
    • Open-Ended Prompts
    • Role-Based Prompts
    • Comparative Prompts
    • Conditional Prompts
    • Summarization prompts
    • Exploratory Prompts
    • Problem-Solving Prompts
    • Clarification Prompts
    • Sequential Prompts
    • Hypothetical Prompts
    • Ethical or Judgment-Based Prompts
    • Diagnostic Prompts
    • Instructional design prompts
    • Page 8
    • Page 7
  • Advanced Prompting Techniques
    • Zero-Shot
    • Few-Shot
    • Chain-of-Thought
    • Meta Prompting
    • Self-Consistency
    • Generated Knowledge
    • Prompt Chaining
    • Tree of Thoughts (ToT)
    • Retrieval-Augmented Generation (RAG)
    • Automatic Prompt Engineer (APE)
    • Active Prompt
    • Directional Stimulus
  • Live Examples
    • Legal
      • Non-Disclosure Agreement (NDA)
      • Employment Contract
      • Lease Agreement
      • Service Agreement
      • Sales Agreement
    • Zero-Shot Prompting
    • Few-Shot Prompting
Powered by GitBook
On this page
  1. Advanced Prompting Techniques

Generated Knowledge

Understanding Generated Knowledge Prompting

What is Generated Knowledge Prompting? Generated Knowledge Prompting (GKP) is a technique where the AI generates intermediate knowledge or context to help it better understand and respond to a prompt. This approach involves splitting the task into two stages: first, generating relevant background information or "knowledge," and second, using that generated knowledge to solve the problem or complete the task.

By guiding the AI to create context before providing a final answer, GKP helps improve the accuracy and depth of responses, especially for complex or nuanced tasks. It mimics how humans often think: gathering knowledge before making a decision or solving a problem.

Examples

Here are practical examples of Generated Knowledge Prompting across various domains:

  1. Explaining Complex Concepts Prompt: "Explain how a blockchain works to someone unfamiliar with technology. First, describe what a blockchain is in simple terms, then explain its purpose." Expected Response:

    1. Generated Knowledge:

      • "A blockchain is a digital ledger that records transactions across a network of computers. It is decentralized, meaning no single person or organization controls it."

    2. Final Answer:

      • "A blockchain ensures secure and transparent record-keeping. For example, in cryptocurrencies, it is used to track who owns what without needing a bank."

  2. Solving Word Problems Prompt: "If a car travels 60 miles per hour for 2 hours, how far does it travel? First, calculate the total time and speed, then determine the distance." Expected Response:

    1. Generated Knowledge:

      • "The car is traveling at 60 miles per hour for 2 hours."

    2. Final Answer:

      • "Distance = Speed × Time = 60 × 2 = 120 miles."

  3. Creative Writing Prompt: "Write a story about a hero who overcomes a great challenge. First, describe the hero's personality and the challenge they face." Expected Response:

    1. Generated Knowledge:

      • "The hero is a shy but intelligent young inventor. The challenge is a drought in their village, which they aim to solve by building a rain-harvesting machine."

    2. Final Answer:

      • "With determination and creativity, the hero builds the machine and saves the village."

  4. Critical Analysis Prompt: "Analyze the impact of remote work on productivity. First, outline the benefits and challenges of remote work." Expected Response:

    1. Generated Knowledge:

      • "Benefits: Flexibility, reduced commuting time, and increased focus for some. Challenges: Isolation, communication difficulties, and distractions at home."

    2. Final Answer:

      • "Remote work impacts productivity based on individual circumstances. While it can boost productivity for focused tasks, it may hinder team collaboration."

Applications

Where and When to Use Generated Knowledge Prompting

  1. Complex or Multi-Step Tasks

    • For problems requiring an in-depth understanding or detailed reasoning. Example: Explaining scientific theories, solving math problems, or writing technical articles.

  2. Creative Generation

    • To develop rich and nuanced creative content like stories, marketing pitches, or design ideas. Example: Generating character backstories for a novel.

  3. Educational Use Cases

    • Teaching or explaining concepts by breaking them into digestible parts. Example: Explaining historical events with context before detailing their outcomes.

  4. Critical Thinking and Analysis

    • For debates, essays, or any task requiring structured reasoning. Example: Analyzing business strategies or summarizing research papers.

  5. Content Summarization

    • When summarizing, the AI can generate an outline or key points before drafting the full summary. Example: Summarizing a report or article.

Troubleshooting

If Things Don’t Work as Expected

  1. The Generated Knowledge Is Irrelevant What to Do:

    • Refine the initial prompt to include specific constraints or focus areas. Example Fix: Change "Explain the concept of AI" to "Explain the concept of AI by first defining artificial intelligence and its applications."

  2. The Final Output Is Incorrect What to Do:

    • Use follow-up prompts to validate the generated knowledge.

    • Adjust the prompt to explicitly connect the knowledge generation step to the task. Example Fix: Instead of "Analyze climate change impacts," use "List key areas affected by climate change, then analyze their economic impacts."

  3. The Knowledge Generation Is Too Brief or Vague What to Do:

    • Ask the AI to expand or provide more detail in the first step. Example Fix: Instead of "Generate a summary," use "Generate a detailed outline of the main arguments before summarizing."

  4. The Process Takes Too Long What to Do:

    • Limit the scope of the task or reduce the level of detail required in the generated knowledge. Example Fix: Change "Explain how space travel works" to "Explain the basic steps of a rocket launch."

Best Practices

  1. Use Clear and Sequential Prompts

    • Clearly define the stages: knowledge generation followed by task execution. Example: "First, list the ingredients needed to bake a cake. Then, explain the steps to bake it."

  2. Encourage Depth in Knowledge Generation

    • Request detailed or categorized knowledge to ensure the AI fully understands the task. Example: "List three advantages and three disadvantages of renewable energy before analyzing its global impact."

  3. Iterate and Validate

    • Review the generated knowledge and refine the prompt if needed before moving to the final step.

  4. Combine with Chain-of-Thought Prompting

    • Use GKP alongside step-by-step reasoning to improve the logical flow. Example: "First, describe the causes of World War II. Then, explain its impact step by step."

Additional Section: Advantages and Limitations

Advantages:

  • Improves task accuracy and quality by ensuring the AI has sufficient context.

  • Handles complex, multi-layered tasks effectively.

  • Encourages structured reasoning and better understanding of nuanced topics.

Limitations:

  • Can increase the time required to generate a response.

  • Requires careful prompt design to avoid irrelevant or redundant knowledge.

  • May produce overly verbose outputs if not guided properly.

Generated Knowledge Prompting is a powerful method for tackling complex tasks, fostering deeper reasoning, and enhancing content generation. By teaching the AI to "think before acting," learners can achieve higher-quality results and build a strong foundation for advanced AI interactions.

PreviousSelf-ConsistencyNextPrompt Chaining

Last updated 5 months ago