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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

Using advanced tracing and evaluation techniques

The process of building an LLM-based application using a tool such as LlamaIndex is very developer-friendly since the framework abstracts away a lot of technical stuff. But at the same time, this complicates things, for the very same reason. When things don’t work as planned, developers need to have effective ways of understanding why. They have to peel back all these layers of abstraction in order to pinpoint the root causes. In other words, we need to be able to see the inner mechanics of our code, understand how different components interact, and be able to identify underlying issues. That’s where tracing becomes a really important feature. On the other hand, because we have so many tools available and so many ways of building our solution, we need a way to benchmark different combinations and determine the best mix of tools and orchestrations. That’s where evaluation comes into play. Evaluation is essential for...

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