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Building Data-Driven Applications with LlamaIndex

You're reading from   Building Data-Driven Applications with LlamaIndex A practical guide to retrieval-augmented generation (RAG) to enhance LLM applications

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781835089507
Length 368 pages
Edition 1st Edition
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Author (1):
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Andrei Gheorghiu Andrei Gheorghiu
Author Profile Icon Andrei Gheorghiu
Andrei Gheorghiu
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Table of Contents (18) Chapters Close

Preface 1. Part 1:Introduction to Generative AI and LlamaIndex FREE CHAPTER
2. Chapter 1: Understanding Large Language Models 3. Chapter 2: LlamaIndex: The Hidden Jewel - An Introduction to the LlamaIndex Ecosystem 4. Part 2: Starting Your First LlamaIndex Project
5. Chapter 3: Kickstarting Your Journey with LlamaIndex 6. Chapter 4: Ingesting Data into Our RAG Workflow 7. Chapter 5: Indexing with LlamaIndex 8. Part 3: Retrieving and Working with Indexed Data
9. Chapter 6: Querying Our Data, Part 1 – Context Retrieval 10. Chapter 7: Querying Our Data, Part 2 – Postprocessing and Response Synthesis 11. Chapter 8: Building Chatbots and Agents with LlamaIndex 12. Part 4: Customization, Prompt Engineering, and Final Words
13. Chapter 9: Customizing and Deploying Our LlamaIndex Project 14. Chapter 10: Prompt Engineering Guidelines and Best Practices 15. Chapter 11: Conclusion and Additional Resources 16. Index 17. Other Books You May Enjoy

The golden rules of prompt engineering

This section is not intended to serve as a definitive guide to prompt engineering. In fact, the field is an ever-expanding one. Since many LLMs are demonstrating emerging capabilities that were not initially anticipated, it is only natural that our methods of interacting with these linguistic experts will also be refined over time. In other words, as LLMs evolve to better model and understand human nature, we in turn learn new ways of interacting with them. In this section, I aim to present some of the most commonly used techniques in prompt engineering, as well as the basic principles that govern the field. As stated in the previous section, writing a good prompt requires a fine balance between several parameters. Here are some of the most important aspects to consider when building prompts for a RAG application.

Accuracy and clarity in expression

The prompt should be clear and precise, avoiding ambiguity. The more clearly you state what...

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