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Building AI Intensive Python Applications

Building AI Intensive Python Applications

By : Rachelle Palmer, Ben Perlmutter, Ashwin Gangadhar, Nicholas Larew, Sigfrido Narváez, Thomas Rueckstiess, Henry Weller, Richmond Alake, Shubham Ranjan
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Building AI Intensive Python Applications

Building AI Intensive Python Applications

By: Rachelle Palmer, Ben Perlmutter, Ashwin Gangadhar, Nicholas Larew, Sigfrido Narváez, Thomas Rueckstiess, Henry Weller, Richmond Alake, Shubham Ranjan

Overview of this book

The era of generative AI is upon us, and this book serves as a roadmap to harness its full potential. With its help, you’ll learn the core components of the AI stack: large language models (LLMs), vector databases, and Python frameworks, and see how these technologies work together to create intelligent applications. The chapters will help you discover best practices for data preparation, model selection, and fine-tuning, and teach you advanced techniques such as retrieval-augmented generation (RAG) to overcome common challenges, such as hallucinations and data leakage. You’ll get a solid understanding of vector databases, implement effective vector search strategies, refine models for accuracy, and optimize performance to achieve impactful results. You’ll also identify and address AI failures to ensure your applications deliver reliable and valuable results. By evaluating and improving the output of LLMs, you’ll be able to enhance their performance and relevance. By the end of this book, you’ll be well-equipped to build sophisticated AI applications that deliver real-world value.
Table of Contents (18 chapters)
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Part 1: Foundations of AI: LLMs, Embedding Models, Vector Databases, and Application Design
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Part 2: Building Your Python Application: Frameworks, Libraries, APIs, and Vector Search
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Part 3: Optimizing AI Applications: Scaling, Fine-Tuning, Troubleshooting, Monitoring, and Analytics
Appendix: Further Reading: Index

Common Failures of Generative AI

If you have just built your generative AI (GenAI) application, then you may be so fascinated by what it can do that you lose sight of answer quality and accuracy. Discovering how often GenAI is incorrect is a challenge in itself.

Many tend to believe that when a computer gives an answer, it gives an accurate answer—usually, more accurate than a human being. For example, most people feel relieved that machines, and not just people, fly airplanes today. Airplanes may be much safer now compared to 15 years ago because of this advancement, but when it comes to GenAI, the results are not nearly as accurate as the onboard systems of a flight craft.

This chapter takes a detailed look at the top five challenges with GenAI applications and why they occur. Understanding these challenges is crucial for developers to devise effective solutions. By the end of this chapter, you will have a good understanding of these challenges, how they influence your...

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