Building Reliable AI Systems (MEAP V07)

Tested strategies to reduce hallucinations, improve performance and cost efficiency, and reduce bias or unethical behavior in your LLMs outputs.

Building Reliable AI Systems shows you exactly how to guide large language models from research prototypes to scalable, robust, and efficient production systems. From model training to maintenance, an engineer will find everything they need to work with LLMs in this one-stop guide.

Inside Building Reliable AI Systems you’ll learn how to:

Deploy LLMs into production
Detect and reduce hallucinations
Mitigate bias
Optimize LLM performance and resource usage
Advanced prompt engineering techniques
Build intelligent agents and Retrieval-Augmented Generation

Building Reliable AI Systems is a guide to putting LLMs into production in the real world. The book bridges the gap between theory and practice. You’ll go beyond basics like prompting into advanced optimizations: intelligent agents, Retrieval Augmented Generation (RAG), and in-depth solutions for mitigating hallucinations and bias.
about the book
Building Reliable AI Systems is a comprehensive guide to creating LLM-based apps that are faster and more accurate. It takes you from training to production and beyond into the ongoing maintenance of an LLM. In each chapter, you’ll find in-depth code samples and hands-on projects—including building a RAG-powered chatbot and an agent created with LangChain. Deploying an LLM can be costly, so you’ll love the performance optimization techniques—prompt optimization, model compression, and quantization—that make your LLMs quicker and more efficient. Throughout, real-world case studies from e-commerce, healthcare, and legal work give concrete examples of how businesses have solved some of LLMs common problems.