Building AI Intensive Python Applications (1 ed)

Master retrieval-augmented generation architecture and fine-tune your AI stack, along with discovering real-world use cases and best practices to create powerful AI apps.

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.

What you will learn

• Understand the architecture and components of the generative AI stack
• Explore the role of vector databases in enhancing AI applications.
• Master Python frameworks for AI development
• Implement vector search in AI applications
• Find out how to effectively evaluate LLM output.
• Overcome common failures and challenges in AI development