Organic Prompting
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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

Adam Russell
Adam Russell

Adam writes about project management on his blog “Adam On Projects” and in his books and other content at Leanpub.com. He is also an expert in applying ChatGPT to project environments and has written several books on ChatGPT.

Adam coaches and mentors other project managers, and consults on various projects around the world.

Adam Russell has successfully delivered software development, software package, and systems integration projects for nearly 40 years, using both Agile and traditional approaches.

Adam started as a software developer, and then moved into pre-sales technical support when the IBM PC launched. He launched his project management career after he helped to close a significant government deal. The head of projects said: “You helped sell it. Now, you can deliver it”.

Since then, Adam has worked for companies ranging from 1-person startups to global tier-1 vendors and customer organisations in roles ranging from hands-on project manager through to General Manager of Systems Integration.

In those roles, Adam has been responsible for delivering hundreds of projects, ranging in budget from $5k up to over $100m. Adam also volunteers his time and skills with non-profit organisations.

Table of Contents

    • Getting Engaged with this Book and its Author
    • Notes about this version
    • Introduction
    • How to Read This Book
      • Outline of this Book
    • Start Here
      • Start with the Problem
      • Start with simplicity
      • First, Action!
      • Second, Critically Evaluate!
      • Crossing the Skills Bootstrapping Chasm
    • Organic Prompting Overview
      • Organic Prompt Design Guidelines
        • Prompt Design Goals
        • Satisficing is Satisfaction
        • Conclusion
      • Organic prompting
        • Organic Prompting Design Parameters
        • Benefits of Organic Prompting
        • Organic Prompting - A Compelling Alternative
        • The Foundations of Organic Prompting
        • Principles of Organic Prompting
        • Example of Organic Prompting
        • Organic Prompt “Nudges”
      • Misunderstanding ChatGPT Weaknesses as Strengths
        • Don’t Mistake the Result with the Function
        • Don’t Mistake the Result with Skills
        • Don’t Mistake the Result with Intelligence
        • The Bottom Line
    • Organic Prompting Framework
      • Organic Prompt Design Guidelines
        • Prompt Design Goals
        • Satisficing is Satisfaction
        • Conclusion
      • The Foundations of Organic Prompting: A Natural Language Approach
        • The Rise of Prompt Engineering
        • Is Prompt Engineering the Only Way?
        • What is “semantic contamination” and how do I avoid it?
        • Impact of Semantic Contamination on Prompting
        • Strategies to Mitigate Semantic Contamination
        • Avoid Semantic Contamination with Organic Prompting
        • Expanding on Techniques
      • Evolutionary Prompt Development
        • Generic Evolutionary Cycle
        • Applying the Generic Evolutionary Cycle to Prompt Development
      • 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
      • 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
      • Interaction Use Cases
        • Overview of Interaction Components
        • Basic ChatGPT Interaction Data Patterns
    • Initial Self-assessment
      • Organic Planning
        • Basic Interacting with ChatGPT
        • From Interaction to I-P-O
        • Self-Assessment - Your Perspective on the World
        • Personal - Domain Knowledge/Experience
        • What role do you expect ChatGPT to play in your solution?
        • Conclusion
        • What’s your solution strategy?
      • Diagnostic Tools
        • Paradigm Zero Mindset
        • Enablement Objective (Automation vs Augmentation)
        • Domain Expertise vs Content Contribution Mix
        • User and AI Domain Expertise
      • Solution-Assessment: Your Approach to Problem Solving
        • Automation vs Augmentation: Choosing the Right AI Strategy
        • Automation/Augmentation Scale Characteristics
      • Solution-Assessment - Content Contribution in the Solution
        • Scale of Contribution Mix of AI-Assisted Content Creation
        • Characteristics of the Scale
      • Solution-Assessment - Intended Distribution Workflow
        • Scale of Workflow Distribution
    • 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
      • 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 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
    • Organic Prompting and Prompt Engineering
      • 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
      • 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
    • Detailed ChatGPT Capability Assessment
      • ChatGPT and Its Capabilities: Analysing Test Performance
        • Introduction
        • Tests Passed by ChatGPT
        • Details of the Tests
        • Inferences from ChatGPT’s Test Performance
        • Conclusion
    • Analysing the Assessments
      • The Impact of Personal Mindset on ChatGPT Use
        • Paradigm Zero and Organic Prompting:
        • Impact on Prompt Development and ChatGPT Interaction
        • Heuristics for Optimising ChatGPT Use
        • Conclusion
      • Domain Expertise vs Content Contribution Mix
      • User and AI Domain Expertise
      • Enablement Objective (Automation vs Augmentation)
      • Response Variability
      • Domain Change & Complexity
      • Accuracy vs Relevance
      • Execution Cycle
    • Wrap & Where to From Here?
    • More from Adam Russell
    • Copyright

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