X-03Retrieval & RAG

Cast a wide net with similarity search, then rerank hard, so only genuinely relevant content spends context budget.

Key insight

Recall is cheap; precision is what protects the window. The reranker — which actually reads the candidates — is where quality comes from.

Failure mode

Stuffing top-20 raw chunks into context: similar-but-wrong documents don’t just waste tokens, they actively mislead the model.

Elsewhere in the atlas