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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

Customizing default prompts

While the default prompts provided by LlamaIndex are designed to work well in most scenarios, there may be instances where customization is necessary or desirable. For example, you might want to adjust prompts to do the following:

  • Incorporate domain-specific knowledge or terminology
  • Adapt prompts to a particular writing style or tone
  • Modify prompts to prioritize certain types of information or outputs
  • Experiment with different prompt structures to optimize performance or quality

By customizing prompts, we can fine-tune the interaction between the RAG components and the language model, potentially leading to improved accuracy, relevance, and overall effectiveness of our application.

The good news is that we can modify the behavior of various LlamaIndex components by supplying our own custom prompt templates. The not-so-good news is that contrary to common expectations, writing a good prompt template is not a trivial task. One...

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