Data foundations for AI
Our RAG system is stale, brittle, or impossible to explain
Treat RAG as an operated data pipeline with a retrieval step.
Retrieval tuning cannot recover content that was never extracted, chunks whose structure was destroyed, metadata that lost its source, documents that were never invalidated, or records the requester was not allowed to see. Most apparent retrieval failures begin earlier in the corpus and index lifecycle.
See the Data Foundations for AI pillar →
Decisions that make the corpus operable
- Ingestion: Define authoritative sources, accepted formats, ownership, trust boundaries, and admission evidence.
- Extraction: Preserve layout and semantic structure, validate independently, and own unresolved exceptions.
- Chunk and metadata integrity: Keep every retrieval unit tied to source identity, position, permissions, version, and transformation.
- Freshness and access: Propagate changes, revocations, and deletions through every derived index under the same scope rules as the corpus.
- Rebuild and evaluation: Make index versions reproducible and evaluate retrieval against production-shaped questions with attributable sources.
Read the RAG reliability path
- RAG is a data pipeline with a retrieval step — move diagnosis upstream from ranking.
- Document extraction is the enterprise RAG bottleneck — preserve structure and meaning during ingestion.
- Vector search is an index, not a database — keep the corpus authoritative and rebuildable.
- Knowledge staleness is an operating concern — make invalidation explicit.
- Poisoning resistance starts at ingestion — enforce trust boundaries before retrieval.
Proof and limits
Inspect the extraction boundary
See how source documents move through extraction, validation, exception handling, and coverage audit before they reach retrieval.