Organic Prompting
Organic Prompting
The natural approach to Prompt & Response Engineering
About the Book
Unlock the full potential of Generative AI tools like ChatGPT with Organic Prompting: The Natural Approach to Prompt & Response Engineering. In this groundbreaking book, Adam Russell shifts the focus from traditional prompt engineering to a more intuitive, natural approach that leverages the power of language as it was meant to be used. Whether you're a seasoned professional or just beginning your journey with AI tools like ChatGPT, Organic Prompting offers a fresh perspective on creating effective, efficient, and human-centric prompts that get results.
Through clear guidelines and practical examples, this book introduces the concepts such as:
- Simplest Effective Prompt (SEP): Focus on crafting the simplest prompt that contains just enough information to achieve the desired response, minimizing unnecessary complexity.
- Minimum Useful Response (MUR): Aim for responses that are just detailed enough to be actionable, avoiding the pursuit of perfection and focusing on practicality.
- Iterative Prompt Refinement: Continuously refine prompts based on the quality of responses received, iterating towards more effective and precise interactions.
- Contextual Framing: Provide clear context within prompts to guide the AI towards generating responses aligned with the intended goals and use cases.
- Outcome-Focused Prompting: Design prompts with a clear end goal, ensuring that each interaction is driven by the desired result rather than the process.
- Satisficing Strategy: Apply a satisficing approach that focuses on achieving satisfactory results quickly rather than optimizing indefinitely for perfection.
- Prompt Evolution: Allow prompts to evolve naturally through use and feedback, adapting to new insights and the changing needs of the task at hand.
- Response Quality Assessment: Develop techniques to evaluate the quality of AI responses based on relevance, accuracy, and usability, guiding further prompt refinement.
- And much, much more...
Discover how to use natural language and strategic thinking to interact with AI in a way that feels less like programming and more like conversation.
Organic Prompting is your essential guide if you're ready to move beyond rigid structures and embrace a more fluid, adaptable way of working with AI. Start with simplicity, let your prompts evolve, and watch as AI transforms from a tool into a true collaborator in your problem-solving process.
Get your copy now and experience a new way of interacting with AI that feels as natural as speaking to a colleague.
About the Author
Table of Contents
- Getting Engaged with this Book and its Author
- Introduction
- Notes about this version
- How to Read This Book
- What is the Prompting Problem?
- But it’s simple, right?
- Why do we get confused with GenAI?
- Limited Product Specifications
- Misunderstanding GenAI Weaknesses as Strengths
- What does this mean, practically.
- Problems with Prompt Engineering
- The Rise of Prompt Engineering
- Is Prompt Engineering the Only Way?
- Prompt Engineering puts all the focus on the Prompt
- Avoiding Semantic Contamination
- The Solution: Organic Prompting
- Start with simplicity
- The First Three Simple Steps with ChatGPT
- First, Action!
- Second, Critically Evaluate!
- Third, Guided Prompt Update!
- Why Use Organic Prompting?
- Benefits of Organic Prompting
- Organic prompting
- A Compelling Alternative to Prompt Engineering
- The Foundations of Organic Prompting
- Principles of Organic Prompting
- Example of Organic Prompting
- Organic Prompt Engineering
- Organic Prompt Guidelines
- Organic Prompt Key Concepts
- Organic Prompt “Nudges”
- Satisficing is Satisfaction
- Conclusion
- Interaction Use Cases
- Overview of Interaction Components
- Basic ChatGPT Interaction Data Patterns
- Prompt Engineering vs. Organic Prompting: A Comparative Analysis
- Why Not Just Start with Prompt Engineering?
- The Pros and Cons of Prompt Engineering
- The Unique Benefits of Organic Prompting
- When is Organic Prompting The Wrong Approach?
- Conclusion
- Bridging Organic Prompting to Prompt Engineering: A Guide for Beginners
- Introduction
- Understanding the Basics of Prompt Engineering
- Core Concepts of Prompt Engineering
- Getting Started with Prompt Engineering
- Step-by-Step Guide
- Conclusion
- Organic Prompt Guidelines
- Organic Response Engineering
- Response Engineering over Prompt Engineering.
- The Problems with a Focus on Prompt Engineering
- An End-to-End Perspective
- What is Response Engineering?
- Scope of Response Engineering
- Evaluation Criteria
- Feedback: Joins the “Fitness Function” to Prompting
- The Definition of Done
- Conclusion
- The Three Primary Dimensions of Response Quality Assessment
- The Context
- Response Quality Assessment
- Diagnostic Matrixes
- Response Assessment Methods
- Common Assessment Elements
- Informal Approach
- Entry-Level Response Quality Assessment
- Basic Assessment: Likert Scale Rubric
- Advanced Assessment: Planguage Framework
- The Anatomy of Response Detail
- Response Quality Assessment Recap
- ChatGPT Doesn’t Write Long
- Response Quality is Asymptotic
- Why is this so?
- What does this mean?
- Response Engineering over Prompt Engineering.
- Putting it all together
- A Prompt Management system
- Putting it all together
- Evolutionary Prompt Development
- Generic Evolutionary Cycle
- Applying the Generic Evolutionary Cycle to Prompt Development
- Evolutionary Development and Improvement of Prompt Efficacy
- Knowledge / Mastery of the Problem Domain
- Skill and Experience with Prompt Structure and Efficacy
- Scope and Capabilities of the End-to-End Solution Construct
- Natural Language Binding
- Riding the Evolutionary Cycle - Keep Working the Edge!
- Conclusion
- Enabling Iterative Prompt Development
- Evolutionary Prompt Development
- The 4C Model of Prompt Intent
- The Purpose of the 4C Model
- The 4C Model Implements / Enables Evolutionary Development
- The Four Categories of the 4C Model
- 1. Context
- 2. Content
- 3. Control
- 4. Construct
- Using the 4C Model
- Conclusion
- The 4C Model of Prompt Intent: Step-by-Step
- 1. Overview
- Initial Assessment:
- Refinement:
- Testing and Iteration:
- Construct Analysis:
- 2. Common Prompt Pitfalls and How to Address Them Using the 4C Model
- Purpose:
- Too Much Context:
- Insufficient Content:
- Over-Controlling the Model:
- Weak Construct:
- 3. Example Prompts with Full 4C Analysis
- Purpose:
- Before and After Prompts:
- 4C Breakdown:
- Results Comparison:
- 4. Templates for Common Prompting Scenarios
- Purpose:
- Categorised by Task Type:
- Flexible Constructs:
- 5. Interactive 4C Checklist for Prompt Review
- Purpose:
- Context:
- Content:
- Control:
- Construct:
- Putting the Checklist to Use:
- 6. Training Materials or Tutorials
- Purpose:
- Visual Breakdown:
- Real-Time Examples:
- Formats for Training Materials:
- Implementation and Usage:
- 7. Edge Cases and Special Considerations
- Purpose:
- Ambiguous Prompts:
- Short vs. Long Prompts:
- Iterative Prompts:
- Conclusion:
- 8. 4C Prompt Engineering Best Practices
- Purpose:
- Balancing the Cs:
- Constructing Iterative Dialogues:
- Adapting to GPT’s Limits:
- Conclusion:
- 9. Success Metrics for 4C-Optimised Prompts
- Purpose:
- Clarity of Response:
- Relevance of Response:
- Efficiency:
- User Satisfaction:
- Conclusion:
- 1. Overview
- Deep-dive into Semantic Contamination
- Impact of Semantic Contamination on Prompting
- Strategies to Mitigate Semantic Contamination
- Avoid Semantic Contamination with Organic Prompting
- Expanding on Techniques
- Bridging Organic Prompting into Prompt Engineering: Understanding Prompt “X-Shot” Patterns
- Introduction
- Zero-Shot Prompts
- Benefits of Zero-Shot Prompts
- One-Shot Prompts
- Few-Shot Prompts
- Multi-Shot Prompts
- Why Use “X-Shot” Patterns?
- Conclusion
- Three Cycles of Interaction with ChatGPT
- 1. Initial Inputs
- Cycle I: Prompt Development and Refinement
- Cycle II: Producing Desired Results
- Cycle III: Seeking External Information
- Fitness Function
- Conclusion
- Response Engineering Response Assessment Patterns
- Assessing ChatGPT Responses
- A Basic Analytical Pattern
- The Relationship Space between Response and Expectation
- The Effect of Experience/Knowledge
- Edge-case Patterns
- A Range of Emotional Responses
- Applying ChatGPT Responses to your Domain
- Planguage Overview and Spreadsheet Setup for Capturing Measures
- What is Planguage?
- Key Elements of Planguage
- Setting Up Planguage Measurements in a Spreadsheet
- Example Spreadsheet Table Layout
- Tips for Effective Spreadsheet Use
- Benefits of Using Planguage with Spreadsheets
- Capturing and Tracking Actual Measurements
- Table Layout for Capturing Measurements
- Column Descriptions
- Example Usage for Trend Detection
- Tips for Spreadsheet Implementation
- Deep-dive into Semantic Contamination
- More from Adam Russell
- Copyright
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