AI & Agentic AI
Building Scalable RAG Systems for Enterprise Knowledge
Dibya Ranjan Mishra· 10 min
A practitioner's guide to designing retrieval-augmented generation pipelines that survive real enterprise data — messy, multilingual, and regulated.
RAG looks deceptively simple in demos. Production RAG is an engineering discipline. We cover ingestion pipelines, semantic chunking, hybrid retrieval, reranking, prompt assembly, and continuous evaluation. We also discuss cost optimization, caching, and the hidden tax of stale embeddings.
This is a working note — expect iteration as the field and our practice evolve. If you have feedback, counterexamples, or want to compare implementation notes, reach out.
Key takeaways
- Chunking strategy matters more than model size.
- Hybrid retrieval (BM25 + dense) outperforms vector-only in most enterprise corpora.
- Evaluation harnesses must be built before, not after, the pipeline.