Why most RAG demos fail in production

Most demos work because the data is tiny, clean, and manually curated. Real enterprise data is the opposite: noisy docs, broken formatting, conflicting versions, and strict access rules. The goal is not "it answers once". The goal is consistent, explainable answers under latency and cost constraints.

The stack that actually works for me

• Ingestion: strict source adapters + metadata normalization
• Chunking: semantic chunking with overlap tuned by doc type
• Retrieval: hybrid search (vector + keyword) with reranking
• Generation: grounded prompts with hard citation requirements
• Evaluation: offline eval set + online feedback loop
• Observability: traces, token cost, retrieval hit quality

Non-negotiable guardrails

• Force citation output format.
• Block answer if confidence is below threshold.
• Log every retrieval set for postmortem analysis.
• Separate user-facing confidence from internal model confidence.

What usually improves quality the fastest

• Better chunk boundaries
• Better reranking
• Better negative examples in evaluation

Before changing models, fix those three first.

Related case studies

• RAG Equity Research Agent — hybrid retrieval with reranking and multi-agent orchestration.
• DAISI enterprise assistant — production RAG on internal process documentation with strict guardrails.