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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
2. Chapter 1: Understanding Large Language Models FREE CHAPTER 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

Key takeaways, final words, and encouragement

The future of generative AI is a complex and rapidly evolving landscape with immense potential for transforming industries, augmenting human capabilities, and driving economic growth. In other words, the future looks bright. However, this future also brings significant technical, ethical, and societal challenges that must be carefully managed to ensure the responsible use of these powerful technologies. As it already happened numerous times in our history, innovation can foster progress and improvement but it can also lead to unintended consequences and disruptions that ripple through society. The rise of generative AI is no exception to this pattern.

While not being a direct contributor to the evolution of generative AI, RAG is definitely a catalyst for accelerating the progress of LLMs. It amplifies the capabilities of even the simplest models, creating new possibilities but also bigger challenges and risks. The software we develop...

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