Production RAG: Enterprise Document Intelligence with pgvector & Reranking
Why simple naive vector search fails in production, and how I built a deterministic, cited document intelligence engine using semantic chunking, PostgreSQL pgvector, cross-encoder reranking, and hallucination guardrails.
The Real Problem with Naive RAG
Many organizations attempt RAG by taking standard 500-token chunks, pushing them into an isolated vector database, and expecting ChatGPT to synthesize flawless answers. In real-world enterprise documents (PDF tables, regulatory disclosures, multi-page legal contracts), this naive approach regularly hallucinates, loses critical context split across chunk boundaries, and produces untraceable answers.
Production Ingestion & Retrieval Pipeline
Why PostgreSQL + pgvector instead of standalone Vector Stores?
Using PostgreSQL with the pgvector extension allowed us to perform hybrid relational + vector queries within a single ACID transaction. For example, filtering by tenant_id, department_id, and document_date before vector calculation prevented cross-client data leakage and avoided paying for multi-tool synchronization pipelines.
Hallucination Elimination & Guardrails
1. Two-Stage Retrieval with Cross-Encoder Reranking
Initial bi-encoder vector search retrieves the top 25 candidates quickly. A cross-encoder reranker then evaluates question-chunk token pairs deeply, eliminating semantic false positives and feeding only the 5 most authoritative excerpts to the context window.
2. Verbatim Footnote Attribution & Verification Loop
The system forces the model to cite exact document IDs, page numbers, and bounding-box coordinates for each claim. A lightweight validation regex inspects the response; if an assertion lacks source text within the retrieved chunk, the claim is rejected.
Production Impact
Achieved over 97% context retrieval precision on complex regulatory audit tests, slashing manual compliance review times from hours to under 2 seconds per query with full source traceability.
Integrating AI or RAG into your production software?
I build enterprise AI systems that deliver deterministic, cited results rather than unpredictable chat prototypes.