RAG in Production: Lessons Learned
By LLMfirst
Retrieval-augmented generation has become the default architecture for enterprise LLM applications. The idea is straightforward: retrieve relevant context, feed it to the model, get a grounded answer.
In practice, every step hides complexity.
Retrieval quality is everything
The single biggest lever is retrieval precision. If you put garbage context in the prompt, no amount of prompt engineering will save you.
- Chunking strategy matters more than embedding model choice. Experiment with chunk size, overlap, and semantic boundaries.
- Hybrid search (vector + keyword) consistently outperforms pure vector search on enterprise data with domain-specific terminology.
- Reranking is not optional. A lightweight cross-encoder reranker after initial retrieval is one of the highest-ROI improvements you can make.
Evaluation must be continuous
A RAG pipeline has many failure modes: retrieval misses, context window overflow, hallucination despite good context, and latency spikes. You need automated evaluation that catches regressions before your users do.
Build a golden dataset of question-answer pairs from real usage. Run it on every deploy.
Start simple, measure, then optimize
The best RAG systems we’ve built started as the simplest possible pipeline and evolved based on measured failures - not architectural astronautics.