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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

Hallucinations

One of the greatest challenges of working with GenAI, and perhaps the most well-known, is hallucination. Hallucination in GenAI refers to the phenomenon where the AI model generates content that sounds plausible but is factually incorrect, nonsensical, or not grounded in the provided input data. This issue is particularly prevalent in natural language processing (NLP) models, such as those used for text generation, but can also occur in other generative models such as image generation and LLMs such as GPT-4.

In the worst case, both the developers and their users do not know whether the answer given by GenAI is correct, partially correct, mostly incorrect, or a complete fabrication.

Causes of hallucinations

Much of the data that organizations capture is either redundant, obsolete, trivial (ROT), or altogether unclassified. As a portion, good data forms a small fraction of the data lakes, warehouses, and databases that most companies have. Whenever beginning your...

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