What are parent-document retrieval and contextual retrieval, and what problems do they solve?

Small chunks retrieve precisely but often lack the surrounding context an LLM needs to answer well. Parent-document (small-to-big) retrieval indexes small chunks for matching, but when a chunk hits, it returns the larger parent section or document that contains it, giving the model fuller context while keeping retrieval precise. Contextual retrieval, popularized by Anthropic, addresses a different problem: an isolated chunk may be ambiguous ('the company grew 3%' — which company, which year?). Before embedding, an LLM prepends a short chunk-specific context blurb situating it within the whole document, which meaningfully reduces retrieval failures, especially when combined with BM25 and reranking. Both trade extra indexing work or storage for better answer quality; contextual retrieval adds one-time LLM cost per chunk (reducible with prompt caching).