AI Consulting / RAG
Architecture, evaluation, and production deployment of RAG systems — including an honest assessment of whether RAG is the right approach for your use case.
What's involved
Getting retrieval right.
RAG vs Fine-tuning Assessment
The most important question is whether RAG is the right tool for your problem. I help teams think through the tradeoffs — latency, cost, accuracy, and maintenance — before committing to an architecture.
Chunking & Embedding Strategy
Retrieval quality is determined upstream. Document parsing, chunking strategy, embedding model selection, and metadata design are the decisions that most directly affect whether RAG systems actually work.
Retrieval Pipeline Design
Designing robust retrieval: vector search, hybrid search, reranking, and query expansion. Building pipelines that handle edge cases, maintain relevance across diverse queries, and degrade gracefully.
Evaluation & Monitoring
RAG systems without evaluation frameworks are guesswork. I help teams build systematic evaluation — retrieval recall, answer faithfulness, hallucination detection — and the monitoring to keep it honest in production.
Approach
Most RAG problems are retrieval problems.
Retrieval-augmented generation is one of the most commonly deployed LLM patterns — and one of the most commonly implemented incorrectly. Most RAG quality problems are retrieval problems, not generation problems.
The question teams should ask before building a RAG system: is the problem really about knowledge retrieval, or is it about reasoning, summarisation, or something else entirely? RAG is not always the answer.
When RAG is the right approach, the architecture decisions that matter most are upstream of the LLM: how documents are parsed, chunked, and embedded, and how the retrieval step is evaluated and iterated.
I have built production RAG systems and have direct experience with the failure modes that tutorials do not cover — query-document mismatch, chunk boundary problems, reranking tradeoffs, and latency under real load.
Work together
Evaluating or improving a RAG system?
Whether you are deciding whether to build a RAG system, debugging one that underperforms, or moving a prototype to production — I can help you get the architecture right.