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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

LLM Output Evaluation

Regardless of the form factor of your intelligent application, you must evaluate your use of large language models (LLMs). The evaluation of a computational system determines the system’s performance, gauges its reliability, and analyzes its security and privacy.

AI systems are non-deterministic. You cannot be certain what an AI system will output until you run an input through it. This means that you must evaluate how the AI system performs on a variety of inputs to have confidence that it performs in line with your requirements. To be able to change the AI system without introducing any unexpected regressions, you also need to have robust evaluations. Evaluations can help catch these regressions before releasing the AI system to customers.

In LLM-powered intelligent applications, evaluations measure the effect of components such as the model chosen and any hyperparameters used with the model, such as temperature, prompting, and retrieval-augmented...

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