RAG in production: retrieval, grounding, and scale

RAG (Retrieval Augmented Generation) grounds LLM answers in your data—docs, knowledge base, or internal systems. Done well, it reduces hallucination and keeps answers up to date. Done poorly, retrieval is slow or irrelevant and the model ignores it.

We focus on three layers: ingestion (chunking, embeddings, indexing), retrieval (similarity search, reranking, hybrid), and generation (prompting, citation, guardrails). We tune each for your domain and latency requirements.

Production RAG also needs monitoring: track retrieval quality, answer relevance, and user feedback so you can iterate on chunks and prompts. We set up dashboards and alerts so issues surface before users do.

We've built RAG systems for support, internal tools, and customer-facing Q&A. If you're taking RAG to production, we can help design the pipeline and evaluate quality.