Why Naive RAG Fails in Production

Naive RAG (what it usually is)

Index chunks, retrieve top-K, and prompt the LLM. It works in demos — then breaks under real traffic, messy documents, and ambiguous questions.

• Chunking based only on tokens, not meaning.
• No evaluation loop.
• No grounding guarantees.

Production-grade RAG checklist

If you implement only a few things, start here:

• Query understanding and routing (FAQ vs exploratory vs lookup).
• Better segmentation (semantic chunks, headers, tables).
• Retrieval diagnostics (why did we pick these passages?).
• Answer verification (citations, confidence, refusals when needed).

Evaluation loops you actually need

RAG is a retrieval system plus a generation system. Measure both.

• Retrieval: recall@K on a labeled set.
• Generation: faithfulness/grounding + helpfulness.
• End-to-end: task success rate.

Where agents help

Agents are useful when the workflow requires tool use, iterative retrieval, or validation — not just a longer prompt.

• Multi-hop retrieval.
• Structured extraction from documents.
• Compare/contrast across sources with verification.

If you want to discuss architecture tradeoffs for your use case, reach out at srivastavark@gmail.com.