Search RAG - Ingestion
How ingestion silently decides retrieval quality through metadata, duplication, freshness, permissions, and provenance.
Article Map
Search RAG Articles
A focused reading list on ingestion, retrieval, generation, permissions, storage, and quality evaluation for production RAG systems.
How ingestion silently decides retrieval quality through metadata, duplication, freshness, permissions, and provenance.
Why chunking is evidence design: semantic boundaries, citable context, and the failure cost of over- or under-chunking.
Where enrichment helps retrieval, where synthetic metadata starts lying, and why source text must remain the evidence.
Why storage must preserve traceability, deletion semantics, permissions, and version history before retrieval can be trusted.
Why permissions must shape retrieval itself: ACL propagation, tenant boundaries, source permissions, and safe indexing rules.
Why retrieval is where RAG systems fail silently, long before the model writes the wrong but convincing answer.
How query rewriting breaks when acronyms, tenants, time, and referents drift away from the user's actual intent.
Why hybrid retrieval is not a checkbox: lexical and vector signals fail differently, and fusion decides which failure wins.
Why reranking must produce a traceable evidence contract, not just a prettier top-k list for the model.
Why generation is rarely the root cause: weak evidence gets amplified into confident, correct-looking wrong answers.
How context packing fails when metadata, citations, and evidence priority are not visible inside the model contract.
Why grounding is mostly refusal discipline: unsupported claims, conflicts, and ambiguity must survive the final answer.
Why the best prose model is not always the best RAG model when citations, contracts, latency, and repair rate matter.
How to measure evidence usage, unsupported claims, answer stability, context efficiency, and production generation health.
Why output-only evaluation is debugging blind: retrieval traces, evidence quality, permissions, and regressions matter first.
The observability that matters when answers look stable but evidence coverage, unsupported claims, or permissions are drifting.
How to detect whether model, prompt, retrieval, reranking, or ingestion changes improved the system or silently degraded it.
Why human review only pays off when reviewer findings become stress tests, regression cases, and trace-level labels.
How to stop tuning prompts for failures that actually came from chunking, retrieval, reranking, or missing evidence.