RAG is a retrieval system
The language model is only the most visible stage. Quality starts at ingestion: well-segmented documents, useful metadata, an update policy, and a search strategy aligned with the questions users actually ask.
Chunking is a semantic decision
Splitting every document by the same character count is simple, but ignores its structure. Good chunks preserve units of meaning—a rule, a procedure, a section—and carry enough context to be understood outside the original document.
More context does not mean a better answer
Sending too many passages increases cost and can dilute relevant evidence. Hybrid search, re-ranking, and metadata filters help produce a small, coherent source set before the model generates any text.
Evaluation is part of the architecture
A reliable system measures retrieval and generation separately. Representative question sets, source traceability, abstention rate, and failure review create the feedback loop required to evolve through evidence rather than intuition.
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