Building RAG Systems — Retrieval-Augmented Generation
Ground your LLM in your data — accurate, cited, hallucination-free.
Retrieval-augmented generation is the most reliable way to make an LLM answer from your own knowledge base instead of making things up. I build RAG systems that go well beyond naive chunk-and-embed: hybrid search, semantic re-ranking, metadata filtering, and citation-grounded answers that hold up in high-stakes domains like finance, healthcare, and law.
What separates a production RAG system from a demo
Most RAG demos retrieve the top-k chunks and hope for the best. Production RAG is an engineering discipline — the quality of retrieval determines the quality of every answer. I tune each stage of the pipeline against your real questions.
- Chunking strategy tuned to your document structure and query patterns
- Hybrid retrieval combining dense embeddings with keyword/BM25 search
- Cross-encoder re-ranking to push the most relevant context to the top
- Metadata filtering, access control, and multi-tenant isolation
- Citation and source-grounding so every answer is traceable
- Retrieval evaluation with recall@k and answer-faithfulness metrics
How I build and evaluate your RAG pipeline
I start by building an evaluation set from your real questions, then measure retrieval and answer quality at every stage. That lets me tune chunking, embeddings, re-ranking, and prompting with evidence rather than guesswork — and prove the system works before it reaches users.
Technologies I use
Related case studies
Frequently asked questions
What is a RAG system?
A RAG (retrieval-augmented generation) system retrieves relevant documents from your knowledge base and feeds them to an LLM as context, so answers are grounded in your data instead of the model's memory. This dramatically reduces hallucinations and lets the model cite sources.
How do you stop RAG systems from hallucinating?
Through better retrieval and strict grounding: hybrid search plus re-ranking to surface the right context, prompts that require citations, and a fallback that refuses to answer when retrieval confidence is low. I measure answer faithfulness so hallucination rates are tracked, not assumed.
Which vector database should I use for RAG?
For most teams pgvector on PostgreSQL is the pragmatic choice — no new infrastructure and strong enough for millions of chunks. For very large or high-QPS workloads I use Pinecone, Qdrant, or Weaviate. I pick based on scale, latency, and your existing stack.
Ready to build with rag systems?
Let's scope your project and turn it into a production system.
Book a discovery call