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Building AI Intensive Python Applications

You're reading from   Building AI Intensive Python Applications Create intelligent apps with LLMs and vector databases

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781836207252
Length 298 pages
Edition 1st Edition
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Table of Contents (18) Chapters Close

Preface 1. Chapter 1: Getting Started with Generative AI FREE CHAPTER 2. Chapter 2: Building Blocks of Intelligent Applications 3. Part 1: Foundations of AI: LLMs, Embedding Models, Vector Databases, and Application Design
4. Chapter 3: Large Language Models 5. Chapter 4: Embedding Models 6. Chapter 5: Vector Databases 7. Chapter 6: AI/ML Application Design 8. Part 2: Building Your Python Application: Frameworks, Libraries, APIs, and Vector Search
9. Chapter 7: Useful Frameworks, Libraries, and APIs 10. Chapter 8: Implementing Vector Search in AI Applications 11. Part 3: Optimizing AI Applications: Scaling, Fine-Tuning, Troubleshooting, Monitoring, and Analytics
12. Chapter 9: LLM Output Evaluation 13. Chapter 10: Refining the Semantic Data Model to Improve Accuracy 14. Chapter 11: Common Failures of Generative AI 15. Chapter 12: Correcting and Optimizing Your Generative AI Application 16. Other Books You May Enjoy Appendix: Further Reading: Index

LLMs – reasoning engines for intelligent apps

LLMs are the key technology of intelligent applications, unlocking whole new classes of AI-powered systems. These models are trained on vast amounts of text data to understand language, generate human-like text, answer questions, and engage in dialogue.

LLMs undergo continuous improvement with the release of new models. featuring billions or trillions of parameters and enhanced reasoning, memory, and multi-modal capabilities.

Use cases for LLM reasoning engines

LLMs have emerged as a powerful general-purpose technology for AI systems, analogous to the central processing unit (CPU) in traditional computing. Much like CPUs, LLMs serve as general-purpose computational engines that can be programmed for many tasks and play a similar role in language-based reasoning and generation. The general-purpose nature of LLMs lets developers use their capabilities for a wide range of reasoning tasks.

A crop of techniques to leverage...

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