RAG - retrieval-augmented generation - grounds a language model in your own data so answers are accurate, current, and citable. I build production RAG systems that retrieve the right context, rank it well, and feed it to the model with the guardrails to keep hallucinations low.
A good RAG system is mostly a good retrieval system. Chunking, embeddings, re-ranking, and evaluation matter more than the prompt - and that is where I spend the engineering effort.
- RAG grounds a model in your own data so answers are accurate, current, and citable - without retraining.
- A good RAG system is mostly a good retrieval system: chunking, embeddings, and re-ranking matter most.
- Grounded generation with citations is what cuts hallucinations and earns user trust.
- Choose RAG when knowledge changes often or must be cited; combine it with fine-tuning when behavior also matters.
Ingestion & chunking
Document pipelines that parse, clean, and chunk your sources intelligently so retrieval returns coherent, high-signal context.
Vector & hybrid search
Embeddings plus keyword and hybrid retrieval with re-ranking, tuned for precision on the questions your users actually ask.
Grounded generation
Prompting and citation strategies that keep the model anchored to retrieved sources and transparent about where answers came from.
RAG evaluation
Retrieval and answer-quality evals so you can measure faithfulness and relevance, and catch regressions as your data grows.
Why retrieval quality decides everything
If the retriever surfaces the wrong context, no prompt can save the answer. So I treat RAG as a retrieval problem first: how documents are chunked, which embedding model fits the domain, whether hybrid search and re-ranking improve precision, and how much context the model actually needs.
On top of solid retrieval, grounded generation and citations make the system trustworthy - users can see the source, and the model is far less likely to invent.
From documents to dependable answers
A typical build covers ingestion, the vector store, retrieval and re-ranking, grounded generation, and an eval harness - wired into your product as an API or chat surface. RAG also pairs naturally with agents, giving an autonomous system a reliable way to fetch knowledge before it acts.
The result is AI that answers from your data, stays current as that data changes, and tells the truth about what it knows.
What is RAG (retrieval-augmented generation)?
RAG is a technique that retrieves relevant information from your own data and supplies it to a language model at generation time. It grounds answers in real sources, keeping them accurate and current without retraining the model.
Does RAG reduce hallucinations?
Yes - by anchoring the model to retrieved, citable context, a well-built RAG system substantially reduces hallucinations. Quality depends on retrieval: good chunking, embeddings, and re-ranking matter more than prompt wording.
When should I use RAG instead of fine-tuning?
RAG is usually the better first choice when knowledge changes often or must be citable, because you update data rather than retrain. Fine-tuning suits fixed style or format needs. The two also combine well.
Ready to build with AI?
Tell me what you are building. I will map the fastest path from idea to a system you can trust in production.
Start a Project