Chunking and Reranking: The Unsung Heroes of Good RAG
Most explanations of retrieval augmented generation focus on the glamorous parts: the embeddings, the vector database, the language model writing the answer. But two quieter steps decide whether the whole thing actually works. Chunking determines what gets stored. Reranking determines what gets used. Get them wrong and even the best model answers from the wrong material.
Chunking: deciding what a passage is
A retrieval system does not store whole documents. It stores pieces, called chunks, and it searches over those pieces. Chunking is the step that cuts your documents up.
That sounds mechanical, but it quietly decides what the system can find. Cut too large, and a chunk contains several unrelated facts. Its embedding becomes a blur of all of them, so it matches nothing precisely, and when it is retrieved it drags irrelevant material into the model's limited context. Cut too small, and a chunk loses the context needed to make sense of it. "Closed on Mondays" is useless if the chunk no longer says what is closed.
What good chunking looks like
The goal is a chunk that is a single, self contained unit of meaning. One fact, one policy, one answer, with enough surrounding words to stand on its own. In practice that often means chunking along natural boundaries: a heading and its paragraph, a single FAQ entry, one row of a table with its labels, rather than slicing every three hundred words regardless of where the sentence falls.
This is also why structure in the source helps so much. A page already written as clear, separate statements chunks itself. A page written as one flowing narrative has to be cut arbitrarily, and arbitrary cuts land in the wrong places.
Retrieval gets you candidates, not answers
When a question comes in, the vector search returns the chunks whose meaning sits closest to it. It is fast and it is broad. It is also approximate. The top twenty results are usually a mix of the right passage, several near misses on the same topic, and a few that matched on a surface similarity. Handing all twenty to the model wastes context and invites confusion.
Reranking: a second, more careful look
Reranking is a second pass that takes those candidates and scores each one specifically against the question, usually with a model built for exactly this comparison. Unlike the first search, which compared the question to every chunk cheaply, the reranker looks at a handful of pairs closely and asks: does this chunk actually answer this question?
The result is a reordered shortlist. The genuinely relevant passage moves to the top, the near misses drop, and only the best few are passed to the language model. Retrieval casts a wide net. Reranking picks the fish.
Why the two together matter
Chunking and reranking are a pair. Good chunks give the reranker clean, single purpose candidates to judge. Good reranking forgives some sloppiness in retrieval by catching the right chunk even when it was ranked fifth. Systems that skip reranking often look fine in demos and degrade in real use, because real questions are messier than demo questions and the top result is not reliably the right one.
What this means for your content
You cannot control how someone else's AI chunks or reranks. You can control whether your content survives the process. Write each important fact as its own clear unit, with enough context that it makes sense alone. Keep related facts close together and unrelated facts apart. Put the specific answer in plain words rather than implying it across a paragraph.
Do that, and your content chunks cleanly, retrieves precisely, and reranks to the top when it is the right answer. Skip it, and the correct information may be on your site and still never reach the model.