Most RAG systems fail quietly — they retrieve the wrong chunk and the model answers from it confidently anyway. Our RAG development work treats retrieval as something you measure and test, not something you set up once and hope holds.
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Retrieval-Augmented Generation gives an LLM access to your documents, your data, your knowledge base — so it can answer from what you actually have, instead of what it memorized during training. Done well, it's the difference between a system that cites your refund policy correctly and one that invents a plausible-sounding version of it.
The gap between a RAG demo and a RAG system you can trust is entirely in the evaluation: knowing exactly how often retrieval pulls the right source, and how often the final answer is actually grounded in it.
Every RAG system we ship is measured against the same three checks before launch.
Chunking strategy is tuned to how your documents are actually structured — not a default splitter that cuts context in half mid-table or mid-clause.
A test set of real queries with known correct sources, scored on whether retrieval found them — tracked as a number, re-run every time the corpus or prompts change.
Every answer is checked against what was actually retrieved, catching the cases where the model asserts something the source documents never said.
The questions we get asked most about RAG development — answered straight, no sales pitch.
RAG gives an LLM access to an external set of documents or data at query time, so it can answer using information it was never trained on — your product docs, support history, or internal wiki — instead of relying only on what it memorized during training. A query retrieves the most relevant chunks of that content first; the model then generates its answer grounded in those chunks, usually citing them, rather than answering from memory alone.
Usually one of three causes: the retriever pulled the wrong chunks and the model answered from them anyway, the chunking strategy split context in a way that lost meaning, or there was no groundedness check to catch the model asserting something the retrieved documents didn't actually say. All three are fixable — but only if you're testing retrieval quality directly, not just eyeballing a few outputs.
A golden set of real queries with known correct answers, scored for whether the right chunks were retrieved and whether the final answer is actually grounded in them. We run this before launch and keep running it as the corpus and prompts change, so retrieval quality is a number you can track, not a feeling.
It depends on corpus size, how messy the source documents are, and whether retrieval needs to span multiple data sources. We price by milestone rather than by the hour, so the number is fixed before work starts. Book a call and we'll scope it in detail.
Yes — retrieval is usually layered onto an LLM system that's already talking to your stack. See our LLM integration work for how we connect AI systems to your existing APIs and data sources.
Book a 30-minute call — we'll tell you honestly whether RAG is the right fit for your data, and what it would take to make it trustworthy.