RAG Readiness Scan
Parse a real PDF into page-level diagnostics before you commit it to a RAG pipeline. See which pages need OCR, which need layout recovery, and which need a manual look — from text extractability, fragmentation, and reading-order signals.
📄 Diagnose the PDF, page by page
Most RAG systems fail in data prep, not the model — and PDFs are the worst offenders. A scanned page has no extractable text, a two-column page comes out scrambled, and a table shatters into fragments. This lab parses a real PDF in your browser and flags, per page, whether it needs OCR, layout recovery, or manual review — before you waste time chunking and indexing it.
Parsed entirely in your browser — the file never leaves this page.
What the scan looks for
The four families of RAG failure
Vocabulary gap & dilution
If a document has no headings or clear sections, chunkers split mid-thought and the right passage never surfaces. Structure is what makes retrieval precise.
Lost in the middle
Oversized chunks bury key facts in the center, where models attend least. Right-sized, well-bounded chunks keep answers grounded.
Multi-intent & scope
Documents that mix many topics without sections make it hard to retrieve for a single question. Clear boundaries help the retriever stay on-scope.
Format & integrity
Tables and unusual formatting get flattened by naive chunkers, silently losing meaning before a model ever sees them.
Built on the RAG AI Factory
This scan mirrors the failure-mode guards from RAG AI Factory — a spec-first pipeline engine that assembles 33 RAG patterns across 9 tiers (chunking, retrieval, query ops, agentic loops) with retrieval, generation, ambiguity, and system guards auto-injected. Declare a pipeline in YAML and the factory builds, guards, and runs it — on AWS Bedrock + Qdrant, with Temporal for durable workflows.
View the engine on GitHubRelated reading · Knowledge Lab