Build a complete RAG system without vectors, embeddings, or vector databases. In this course, you’ll build BM25 for lexical search, PageIndex for document navigation, and a complete retrieval pipeline that works on real PDFs. No API keys. No cloud costs. No expensive vector infrastructure. So you can experiment freely without worrying about the API cost. You’ll understand what is happening underneath and will be able to debug or improve the retrieval engine — because you build the retrieval components yourself. You’ll build them. You’ll tune them. You’ll benchmark them.
And you’ll learn how to determine why one retrieval strategy performs better than another. You’ll learn how to handle real-world document retrieval, tune BM25 parameters, navigate large structured documents, and improve retrieval when a basic search approach isn’t giving you satisfactory results. You’ll also learn how different retrieval techniques behave on different types of documents and queries, helping you make better engineering decisions instead of simply choosing a retrieval method by default. By the end, you won’t just know how to build a RAG application. You’ll understand how to engineer the retrieval system behind it.
