Build an Advanced RAG Application

Retrieval Augmented Generation—RAG—is now the standard way to improve LLM accuracy and relevance. But building production-grade RAG systems requires far more than connecting an LLM to a vector database. In Build an Advanced RAG Application (From Scratch), you’ll learn RAG from first principles by creating a complete portfolio of end-to-end applications. You’ll build each component of the pipeline, ensuring full control over every part of the stack.

Written by former Google research scientist Hamza Farooq, this hands-on guide takes you from LLM and transformer fundamentals through keyword search and semantic retrieval to production RAG systems. You’ll build a hotel search engine with semantic ranking, implement semantic caching for cost-effective production deployments, develop autonomous AI agents powered by RAG context, and deploy optimized open-source LLMs. Through under-the-hood experience, you’ll master embeddings, chunking, reranking, vector databases, evaluation frameworks, fine-tuning, and more.