Chunking decides what your retriever can ever surface. Get the boundaries wrong and no embedding model, no reranker, no LLM can repair the damage downstream.
Implement five chunking strategies from scratch: fixed-window, sentence, recursive-split, semantic clustering, and structural markdown headers.
Measure recall@k on a fixture corpus with gold-labeled answer spans and explain why one strategy wins on prose and a different strategy wins on technical documents.
Read a chunk-length distribution and recognize the failure modes each strategy injects: orphan sentences, mid-symbol cuts, header-only chunks, semantic drift.
Pick a default for a new corpus without running the benchmark by inspecting three properties: document type, average paragraph length, and whether the format carries explicit structure.
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