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Unlocking the Secrets of Prompt Engineering

You're reading from   Unlocking the Secrets of Prompt Engineering Master the art of creative language generation to accelerate your journey from novice to pro

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781835083833
Length 316 pages
Edition 1st Edition
Concepts
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Author (1):
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Gilbert Mizrahi Gilbert Mizrahi
Author Profile Icon Gilbert Mizrahi
Gilbert Mizrahi
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Table of Contents (18) Chapters Close

Preface 1. Part 1:Introduction to Prompt Engineering FREE CHAPTER
2. Chapter 1: Understanding Prompting and Prompt Techniques 3. Chapter 2: Generating Text with AI for Content Creation 4. Part 2:Basic Prompt Engineering Techniques
5. Chapter 3: Creating and Promoting a Podcast Using ChatGPT and Other Practical Examples 6. Chapter 4: LLMs for Creative Writing 7. Chapter 5: Unlocking Insights from Unstructured Text – AI Techniques for Text Analysis 8. Part 3: Advanced Use Cases for Different Industries
9. Chapter 6: Applications of LLMs in Education and Law 10. Chapter 7: The Rise of AI Pair Programmers – Teaming Up with Intelligent Assistants for Better Code 11. Chapter 8: AI for Chatbots 12. Chapter 9: Building Smarter Systems – Advanced LLM Integrations 13. Part 4:Ethics, Limitations, and Future Developments
14. Chapter 10: Generative AI – Emerging Issues at the Intersection of Ethics and Innovation 15. Chapter 11: Conclusion 16. Index 17. Other Books You May Enjoy

How to approach prompt engineering (experimentation)

Approaching prompt engineering involves a systematic and iterative process of experimentation to achieve the desired output from an LLM. The key is to refine and adapt your prompt based on the model’s responses, continually improving its effectiveness. Here are the steps to approach prompt engineering through experimentation:

  1. Define the objective: Clearly outline the goal of the interaction with the LLM. Determine the specific information, format, and context required for the desired output.
  2. Craft the initial prompt: Using the components of a prompt, such as context, instruction, role prompting, examples, and output pattern, create a clear and concise prompt that communicates your expectations and requirements to the LLM.
  3. Adjust LLM parameters: Set the initial values for LLM parameters, such as temperature, top-k, and max tokens, based on your output preferences, such as creativity, determinism, and response...
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