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
Key Features
- Get to grips with the fundamentals of LLMs, vector databases, and Python frameworks
- Implement effective retrieval-augmented generation strategies with MongoDB Atlas
- Optimize AI models for performance and accuracy with model compression and deployment optimization
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description
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
Who this book is for
This book is for software engineers and developers looking to build intelligent applications using generative AI. While the book is suitable for beginners, a basic understanding of Python programming is required to make the most of it.
Table of Contents
- Getting Started with Generative AI
- Building Blocks of Intelligent Applications
- Large Language Models
- Embedding Models
- Vector Databases
- AI/ML Application Design
- Useful Frameworks, Libraries, and APIs
- Implementing Vector Search in AI Applications
- LLM Output Evaluation
- Refining the Semantic Data Model to Improve Accuracy
- Common Failures of Generative AI
- Correcting and Optimizing Your Generative AI Application
Rachelle Palmer is the Product Leader for Developer Database Experience and Developer Education at MongoDB, overseeing the driver client libraries, documentation, framework integrations, and MongoDB University. She has built sample applications for MongoDB in Java, PHP, Rust, Python, Node.js, and Ruby. Rachelle joined MongoDB in 2013 and was previously the Director of the Technical Services Engineering team, creating and managing the team that provided support and CloudOps to MongoDB Atlas.
Ben Perlmutter is a Senior Engineer on the Education AI team at MongoDB. He applies AI technologies such as LLMs, embedding models, and vector databases to improve MongoDB's educational experience. His team built the MongoDB AI chatbot, which uses RAG to help thousands of users a week learn about MongoDB. Ben formerly worked as a technical writer specializing in developer-focused documentation.
Ashwin Gangadhar is a Senior Solutions Architect at MongoDB with over a decade of experience in data-driven solutions for e-commerce, HR analytics, and finance. He holds a master's in Controls and Signal Processing and specializes in search relevancy, computer vision, and NLP. Passionate about continuous learning, Ashwin explores new technologies and innovative solutions. Born and raised in Bengaluru, India, he enjoys traveling, exploring cultures through cuisine, and playing the guitar.
