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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

Summary

Now that you have navigated through these GenAI challenges, you can appreciate some of the complexities and nuances that accompany these powerful technologies. The issues of hallucinations, sycophancy, data leakage, cost, and performance present formidable obstacles that demand a critical eye and innovative solutions. Each challenge offers a unique perspective on the limitations and potential pitfalls inherent in GenAI applications.

Despite these hurdles, GenAI remains unequivocally valuable. It continues to transform industries, enhance productivity, and open new avenues for creativity and innovation. By understanding and addressing these challenges, developers can harness the full potential of GenAI, delivering robust, reliable, and responsible applications. At the same time, it’s also important to note that applications can be useful even when they are not always correct. To take ChatGPT as an example: it has greatly improved the productivity of millions of users...

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