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.
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 Prompt Design Guidelines
- 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
- Organic Prompt Design Guidelines
- 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
- Organic Planning
- 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
- Response Engineering over Prompt Engineering.
- 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
- Prompt Engineering vs. Organic Prompting: A Comparative Analysis
- 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
- ChatGPT and Its Capabilities: Analysing Test Performance
- 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
- The Impact of Personal Mindset on ChatGPT Use
- Wrap & Where to From Here?
- More from Adam Russell
- Copyright
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