Chunking, Indexing, and Retrieval Strategy for a RAG Pipeline
Explore how to design each stage of a retrieval-augmented generation pipeline, including chunking source documents, embedding chunks, and indexing for fast retrieval. Understand matching retrieval strategies to data structures and query types to ensure accurate, grounded AI-generated answers. Learn to diagnose common failure modes like stale indexes and mismatched embeddings to build reliable RAG systems.
Policy check has been treated, so far, as if the relevant policy sections simply arrive when needed. They do not arrive on their own. Something has to chunk the lending manual into retrievable pieces, index those pieces so a query can find the right ones quickly, and retrieve the specific pieces a given question actually needs, all before the model ever sees a grounded answer to write. This lesson designs that pipeline properly, because each of those stages is an independent decision, and a mistake in any one of them produces a system that runs without error while quietly returning the wrong material. In this lesson, we will cover:
What actually happens, stage by stage, between a source document and a grounded answer
Choosing a chunking strategy that follows the structure of the source material instead of an arbitrary size
Matching retrieval strategy to data shape and query pattern, rather than defaulting to one universal approach
The two RAG failure modes that produce a confident, well-formed, and wrong answer with no error thrown anywhere
What a RAG pipeline actually does, end to end
A retrieval-augmented generation pipeline is not a single feature. It is a sequence of independent decisions, each with its own way of going wrong. Source documents are split into chunks, the retrievable units the rest of the pipeline works with. Each chunk is embedded, converted into a vector representation that captures something about its meaning. The chunks and their embeddings are indexed, organized so that a search at query time can find the relevant ones quickly rather than scanning everything. At query time, the incoming question is itself embedded, and the index is searched to retrieve the chunks judged most relevant. Those retrieved chunks, along with the original question, go to the model, which generates an answer grounded in what was actually retrieved, not in whatever the manual happens to say ...
