Design LLM search or RAG for very long documents and very long user queries.

Treat the context window as a budget. For long documents, chunk along their structure and index chunks plus document summaries, so retrieval goes coarse to fine. For long queries, have a model rewrite them into focused sub-queries, retrieve for each, then merge and rerank.

Pack the best chunks up to a token budget, with the most relevant at the start or end, since models use the middle of long contexts worst. When the answer needs a whole document, use map-reduce: extract per chunk, then answer over the extracts.