Answers
What is RAG (retrieval-augmented generation)?
In short
RAG (retrieval-augmented generation) grounds an LLM's answers in your own documents and data by retrieving relevant context at query time and passing it to the model.
Short answer
RAG (retrieval-augmented generation) grounds an LLM's answers in your own documents and data by retrieving relevant context at query time and passing it to the model.
What this actually means in practice
It's how you stop a generic LLM being wrong about your business in subtle, expensive ways. The hard parts are ingestion, chunking, retrieval relevance, evaluation and content lifecycle — the LLM is the easy part.
The most common pitfall
RAG built on a dump of PDFs with no chunking, no eval and no content workflow.
What to do next
Define a small eval set and measure retrieval relevance before building the LLM layer.
Frequently asked questions
Does Forth Systems help with this?
Yes — Forth Systems works with banks, payment institutions, insurers and infrastructure operators on exactly this kind of work. Engagements start with a fixed-scope assessment so you see the shape before committing.
How experienced is the team?
Engagements are staffed by named, UK-based senior engineers — not a rotating offshore pool. References from the second line of comparable clients are available on request.
Where are you based?
Edinburgh-based, delivering UK-wide with onsite presence in London and across Scotland as required.
How fast can we start?
Most engagements start within 2-4 weeks of a signed SoW, faster where an existing supplier framework is in place.