Shipping a RAG Chatbot to Production — Retrieval, Evals, Costs
Retrieval quality decides everything
Demos impress with 10 documents; production drowns in 10,000. Chunk by semantic boundary (sections, not fixed tokens), embed with metadata filters (tenant, date, source), and hybrid-search (vector + BM25) for keyword-heavy corpora. Measure retrieval precision separately from generation quality — conflating them hides the real failure.
Evals before launch
Build a 50-question golden set with expected answers before tuning anything. Every prompt or chunking change re-runs the set; regressions block merges like failing tests. Add adversarial cases (contradictory docs, out-of-scope questions) from day one.
Guardrails and cost control
System prompts constrain scope, PII redaction runs pre-retrieval, and human-in-the-loop gates consequential actions. Budget per query (input + output tokens × margin) and alert on drift — one viral feature can 10x your inference bill overnight. Cache frequent answers aggressively.
The honest timeline
Prototype in days, production in 6–10 weeks. The gap is evals, guardrails, observability, and the long tail of edge-case documents. Plan for it or launch a demo wearing a production costume.
Related Articles
AI Guardrails for Production — PII, Jailbreaks, and Human Review
Layered defenses for LLM apps: input filters, output checks, and escalation paths that actually work.
Choosing a Vector Database — pgvector vs Pinecone vs Qdrant
Scale thresholds, filtering needs, and ops burden: picking vector storage without over-engineering.
Document Extraction Pipelines — From PDFs to Structured Data
OCR, layout-aware chunking, and validation loops for invoices, resumes, and lab reports.
Written by AI Engineer
Specialized engineering teams at Omnetra focus on writing high-performance code, ensuring API security, and optimizing layouts for client success.