Building Production RAG Systems: Lessons from the Field

Most RAG tutorials make it look simple: chunk your documents, embed them, store them in a vector database, and query with an LLM. In practice, building a RAG system that works reliably in production is a different challenge entirely.

After building multiple RAG systems — from document analysis platforms to email support assistants — here are the patterns and lessons I keep coming back to.

The Gap Between Demo and Production

A demo RAG system needs to answer a few questions correctly. A production RAG system needs to:

• Handle documents it's never seen before
• Return "I don't know" when it should
• Track where every answer came from (citations)
• Stay within cost and latency budgets
• Degrade gracefully when the retrieval fails

These are fundamentally different requirements, and they demand different engineering decisions.

Chunking Strategy Matters More Than You Think

The most impactful decision in a RAG pipeline isn't which embedding model to use — it's how you chunk your documents.

I've found that semantic chunking (splitting based on meaning boundaries) consistently outperforms fixed-size chunking. But even more important is metadata-aware chunking: preserving section headers, document titles, and structural context alongside each chunk.

# Instead of raw text chunks, preserve structure
chunk = {
    "text": "The retrieval pipeline processes...",
    "metadata": {
        "document": "architecture-guide.pdf",
        "section": "Retrieval Pipeline",
        "page": 12,
        "chunk_index": 3
    }
}

This metadata becomes essential for citation tracking, filtering, and debugging retrieval quality issues.

Hybrid Retrieval is Worth the Complexity

Pure semantic search (vector similarity) has a well-known weakness: it can miss exact keyword matches that a user expects to find. Conversely, keyword search misses semantic relationships.

In production, I combine both:

1. Semantic retrieval via vector embeddings (FAISS, pgvector)
2. Keyword retrieval via BM25 or full-text search
3. Reciprocal rank fusion to merge and re-rank results

The improvement in recall is significant, especially for domain-specific terminology that embedding models may not handle well.

Cost and Latency Are First-Class Concerns

Every LLM call has a cost. Every retrieval query has latency. In production, these add up fast.

Practical strategies I use:

• Cache frequently asked queries — many users ask similar questions
• Tiered retrieval — fast keyword search first, expensive re-ranking only when needed
• Token budgeting — set hard limits on context window usage per query
• Track everything — log token counts, latency percentiles, and cost per query

If you can't measure it, you can't optimize it.

Human-in-the-Loop is Not Optional

For any RAG system where accuracy matters (and when doesn't it?), you need a human review layer:

• Confidence scoring on retrieval results
• Flagging low-confidence answers for review
• Feedback loops where users can correct or validate responses
• Audit trails showing which documents informed each answer

This isn't just about quality — it's about trust. Users need to know they can verify and override AI outputs.

What I'd Build Differently Now

If I were starting a new RAG system today, I'd prioritize:

1. Evaluation from day one — automated tests for retrieval quality, not just "does it run"
2. Observability built in — structured logging of every retrieval and generation step
3. Graceful degradation — explicit handling of "no relevant documents found"
4. Version control for embeddings — so you can re-index and compare quality across versions

The RAG space is moving fast, but these production fundamentals stay constant regardless of which model or vector store you use.

This is the first in a series of articles on building production AI systems. Next up: designing agentic workflows with guardrails.