Retrieval-Augmented Generation (RAG) has become the go-to pattern for grounding large language models in enterprise knowledge. But there's a significant gap between the RAG demos you see in tutorials and what it takes to run RAG at enterprise scale.
The Chunking Problem
Every RAG tutorial starts with "split your documents into chunks." What they don't tell you is that chunking strategy will make or break your system.
Semantic coherence matters more than chunk size. A 500-token chunk that contains a complete thought will outperform a 1000-token chunk that cuts off mid-paragraph.
Overlap is essential, but expensive. Overlapping chunks ensure you don't lose context at boundaries, but they also increase your embedding costs and storage.
Metadata is your friend. Rich metadata enables filtering and re-ranking that dramatically improves relevance.
Embedding Quality vs. Speed
When you're embedding millions of documents, the choice of embedding model becomes consequential:
- Accuracy vs. cost: Larger models produce better results but cost more.
- Batch processing: You need infrastructure for continuous embedding.
- Versioning: Plan for re-embedding when you improve your strategy.
Retrieval is Harder Than It Looks
Basic cosine similarity gets you started but won't get you to production quality:
Hybrid search is essential. Combining semantic and keyword search catches both conceptual matches and exact terminology.
Re-ranking improves precision. A two-stage pipeline consistently outperforms single-stage retrieval.
Query understanding matters. Query expansion, entity recognition, and intent classification all help.
Lessons Learned
- Start with evaluation infrastructure. You can't improve what you can't measure.
- Invest in data quality. The best architecture can't compensate for poor data.
- Plan for iteration. Your first version will be wrong.
- User feedback is gold. Capture and use every thumbs up or down.
Building RAG at scale is an engineering challenge as much as an AI challenge.