Building Production-Ready RAG Systems: What Nobody Tells You

Everyone's building RAG systems these days. Vector databases are the new hotness, and every consultant with a ChatGPT account thinks they're an AI architect. But here's the thing—most of what you read online about RAG systems is dangerously incomplete.

We've deployed RAG systems for enterprises with millions of documents, strict compliance requirements, and zero tolerance for "it usually works." Here's what we've learned.

The Demo Trap#

Your RAG demo works great with 1,000 documents. It answers questions accurately, retrieval is fast, and stakeholders are impressed. Then you load production data—500,000 documents across 47 different formats—and everything falls apart.

The issues that surface at scale:

• Retrieval precision tanks when your corpus grows
• Latency spikes make the system unusable
• Hallucinations increase as context windows get polluted with noise
• Costs explode because you didn't plan for embedding compute

Chunking Strategy Matters More Than Your Model#

Everyone obsesses over which LLM to use. Few spend enough time on chunking strategy—and it's often the difference between a system that works and one that doesn't.

# Bad: Fixed-size chunks ignore document structure
chunks = split_text(document, chunk_size=512)

# Better: Structure-aware chunking
def semantic_chunker(document):
    # Respect document boundaries
    sections = extract_sections(document)

    chunks = []
    for section in sections:
        # Keep related content together
        if len(section) <= MAX_CHUNK_SIZE:
            chunks.append(section)
        else:
            # Split at paragraph boundaries, not arbitrary positions
            chunks.extend(split_at_paragraphs(section))

    return chunks

The key insight: chunks should be semantic units, not arbitrary text slices.

Hybrid Search Is Non-Negotiable#

Pure vector search has a fatal flaw: it can't find what it doesn't semantically understand. Proper nouns, technical terms, and exact phrases often need keyword matching.

Our typical setup:

1. Vector search for semantic similarity (top 20 candidates)
2. BM25 for keyword matching (top 20 candidates)
3. Reciprocal rank fusion to merge results
4. Re-ranking with a cross-encoder for final ordering

The Metadata You're Not Collecting#

Every document in your RAG system needs rich metadata. Not just title and date—everything that helps with filtering and retrieval.

Essential metadata:

• Document type (policy, procedure, email, report)
• Department/source (legal, HR, engineering)
• Date ranges (effective date, expiration date)
• Access level (who can query this?)
• Confidence score (how reliable is this source?)

interface DocumentMetadata {
  id: string;
  source: string;
  documentType: 'policy' | 'procedure' | 'reference' | 'correspondence';
  department: string;
  effectiveDate: Date;
  expirationDate?: Date;
  accessLevel: 'public' | 'internal' | 'confidential';
  lastVerified: Date;
  version: string;
}

Evaluation: The Hard Part#

You built your RAG system. It seems to work. But how do you know it's actually good?

Build an evaluation framework before you go to production:

1. Golden dataset: Hand-curated question-answer pairs
2. Retrieval metrics: Precision@K, Recall@K, MRR
3. Generation metrics: Faithfulness, relevance, coherence
4. Human evaluation: Regular sampling and expert review

What Success Actually Looks Like#

A production-ready RAG system isn't the one with the best benchmark scores. It's the one that:

• Handles edge cases gracefully
• Admits when it doesn't know
• Maintains performance at scale
• Has clear observability and debugging
• Can be updated without downtime

The journey from demo to production is longer than most teams anticipate. But with the right approach—respecting complexity, investing in infrastructure, and measuring relentlessly—you can build systems that actually deliver value.

Need help with your RAG implementation? Let's talk.