The first three parts of this series covered why production RAG systems fail and how the quality of the data foundation directly affects everything that comes after it. We looked at document ingestion, parsing, chunking, and metadata design—the layers responsible for turning raw information into something a retrieval system can actually work with.
Then we moved into retrieval itself. We saw why vector search alone is often insufficient, how semantic and lexical search complement each other, and how reranking can turn a large set of possible matches into a small set of highly relevant documents.
But even perfect retrieval means nothing if the LLM cannot use it properly.
That is where this part begins.
In Part 4, we move from retrieval to generation. We will look at what happens after the system has found the right chunks: how to compress context without losing meaning, how to construct prompts that keep the model grounded, and how to evaluate whether the entire pipeline is actually working.
