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
- Introduction
- Notes about this version
- How to Read This Book
- Takeways from Part 1
- Summary
- Key Concepts to Apply
- Techniques to Practice
- Other Takeaways
- Prompting Problems
- Basic Misconceptions about LLMs
- Misunderstanding the Interaction Model
- Limited Product Specifications
- LLMs Flip Standard SaaS Expectations
- Why do we get confused with GenAI?
- Limited Product Specifications
- Misunderstanding GenAI Weaknesses as Strengths
- LLMs are not Intelligent.
- Problems with Prompt Engineering
- The Rise of Prompt Engineering
- Prompt Engineering is a Heavyweight Tool
- Prompt Engineering puts all the focus on the Prompt
- The Quest for Control
- The Language is Natural, Baby!
- The Problem of Semantic Contamination
- Basic Misconceptions about LLMs
- The Solution: Organic Prompting
- Start with simplicity
- The First Three Simple Steps
- First, Prompt!
- Second, Critically Evaluate the Response
- Third, Guided Prompt Update
- Lastly, Knowing when you’re done
- Conclusion
- What is Organic Prompting?
- A Compelling Alternative to Prompt Engineering
- Principles of Organic Prompting
- Example of Organic Prompting
- What Is Role Should ChatGPT Play in Your Life?
- Job Role
- Relationship Role
- Conclusion
- Takeways from Part 2
- Summary
- Key Concepts to Apply
- Techniques to Practice
- Other Takeaways
- Organic Prompt Engineering
- Organic Prompt Guidelines
- Organic Prompt Key Concepts
- The Desire for Control
- 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
- Conclusion
- 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.
- Takeways from Part 3
- Summary
- Key Concepts to Apply
- Techniques to Practice
- Other Takeaways
- Evolutionary Prompt Development
- Prompt Evolutionary Cycles
- Generic Evolutionary Cycle
- Applying the Generic Evolutionary Cycle to Prompt Development
- A Roadmap for Evolutionary Development
- Knowledge: Mastery of the Problem Domain
- Skill and Experience with Prompt Structure and Efficacy
- The End-to-End Solution Construct
- Natural Language Binding
- Riding the Evolutionary Cycle - Keep Working the Edge!
- Conclusion
- Prompt Evolutionary Cycles
- Quality Analysis & Diagnostics
- The Impact of Personal Mindset on ChatGPT Use
- Paradigm Zero and Organic Prompting
- How does Paradigm Zero Affect on Prompt Development?
- Heuristics for Optimising ChatGPT Use
- Conclusion
- Domain Mastery vs Content Contribution Mix
- Scales
- Analysis Matrices
- User and AI Domain Expertise
- Scales
- Analysis Matrices
- Enablement Objective (Automation vs Augmentation)
- Scales
- Analysis Matrices
- Response Variability
- Scales
- Analysis Matrices
- The Impact of Personal Mindset on ChatGPT Use
- The 4C Model of Prompt Intent
- Overview
- The Purpose of the 4C Model
- The Four Categories of the 4C Model
- Context
- Content
- Control
- Construct
- Using the 4C Model
- Using the 4C Model to Evaluate Prompt Design
- Evaluating the Response Using the 4C Model
- On the Quest for Control
- Implementing the Evolutionary Prompt Cycle
- Connecting Response to Prompt
- Example
- Conclusion
- More from Adam Russell
- Copyright
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