RAG Systems in Production

Practical insights on deploying Retrieval-Augmented Generation systems at scale, from chunking strategies to evaluation metrics.

1 min read
rag
ai
llm
production

RAG Systems in Production

After deploying RAG systems handling thousands of queries daily, here's what actually matters in production.

Chunking is Everything

Your retrieval quality depends more on chunking strategy than model choice.

def smart_chunk(document):
    # Preserve semantic boundaries
    chunks = split_by_headings(document)
    # Add overlap for context
    chunks = add_overlap(chunks, tokens=50)
    # Include metadata
    return enrich_with_metadata(chunks)

Embedding Selection

ModelSpeedQualityCost
OpenAI ada-002FastGood$$
CohereMediumGood$$
Local (e5)SlowGoodFree

Retrieval Tuning

  1. Start with simple cosine similarity
  2. Add hybrid search (BM25 + semantic)
  3. Implement re-ranking for top-k results
  4. Use query expansion for ambiguous questions

Evaluation Metrics

  • Retrieval: Recall@k, MRR
  • Generation: Faithfulness, Answer relevance
  • End-to-end: User satisfaction, resolution rate

Production Gotchas

  • Cache embeddings aggressively
  • Monitor for drift in query patterns
  • Build feedback loops from day one
  • Have fallback strategies for retrieval failures

RAG is not plug-and-play. It requires continuous tuning based on your specific domain.