RAG in Production: Beyond the Tutorial
Building a RAG system that works in a demo is easy. Building one that works in production is an entirely different challenge. Here's what you need to know.
The RAG Reality Check
Every tutorial makes RAG look simple: chunk your documents, embed them, store in a vector database, retrieve, and generate. Five steps, twenty lines of code, and you have a working system.
Except you don't. You have a demo that works on cherry-picked examples. Production RAG is a different beast entirely.
What Tutorials Don't Tell You
Chunking Strategy Matters More Than Your Model
The most common mistake in RAG systems is naive chunking. Splitting documents by character count or even sentence boundaries destroys context and leads to poor retrieval quality.
Instead, consider:
- Semantic chunking: Split at natural topic boundaries
- Hierarchical chunking: Maintain parent-child relationships between chunks
- Overlapping windows: Preserve context at chunk boundaries
- Metadata enrichment: Attach source, section, and relationship data to every chunk
Retrieval Is Not Just Vector Search
Pure vector similarity search gets you 60-70% of the way. For production quality, you need hybrid retrieval:
- Vector search for semantic similarity
- Keyword search (BM25) for exact matches
- Metadata filtering for scope constraints
- Re-ranking for precision improvement
Evaluation Is Non-Negotiable
You cannot improve what you cannot measure. Every production RAG system needs:
- Retrieval metrics: Precision, recall, and NDCG at various k values
- Generation metrics: Faithfulness, relevance, and coherence scores
- End-to-end metrics: User satisfaction and task completion rates
- Regression testing: Automated test suites that catch quality degradation
Architecture for Production
A production RAG system is not a single pipeline. It's an ecosystem of components:
- Ingestion pipeline: Document processing, chunking, embedding, indexing
- Retrieval engine: Hybrid search with re-ranking
- Generation layer: Prompt engineering with guardrails
- Evaluation framework: Continuous quality monitoring
- Feedback loop: User feedback driving improvements
The GDPR Factor
For European enterprises, GDPR compliance adds another layer of complexity:
- Where is your data stored and processed?
- Can you delete specific user data from your vector store?
- How do you handle data retention policies?
- Are your LLM API calls compliant with data processing agreements?
These aren't afterthoughts — they need to be part of your architecture from day one.
Getting Started
If you're building RAG for production, start with the fundamentals: solid chunking, hybrid retrieval, and comprehensive evaluation. The model and the framework matter far less than these engineering decisions.