Why it matters
A RAG pipeline is the conveyor belt from raw documents to answer-ready chunks. Weak links anywhere produce systemic errors: bad OCR, naive chunking, stale indexes, missing filters, or context overflow at generation time.
Teams debug pipelines poorly when they only inspect final answers. You need stage-level visibility.
How it works
Typical stages: extract, clean, chunk, enrich metadata, embed, index, retrieve, rerank, assemble context, generate, cite, log. Each stage has inputs, outputs, and failure modes.
Production pipelines version configs, support rollback, and separate batch indexing from online query paths.
Example
Nintendo pipeline ingests Help Center HTML nightly and on webhook for Refund Policy changes. Chunk boundaries follow H2 headings so citations land on specific sections. Failed embedding jobs quarantine documents instead of partial indexing.
Common mistakes
- 1No quarantine for failed ingestion rows
- 2Same chunk settings for policy PDFs and API reference tables
- 3Online queries hitting indexes mid-rebuild without read consistency
- 4Pipeline logs that omit collection and document version IDs
Definitions are useful. Governed knowledge is better.
Wiki helps you turn these concepts into a real system for the knowledge behind your AI.
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