What RAG means in plain language
RAG combines two steps. First, the system searches your approved documents for the passages most relevant to a question. Second, it gives those passages to the language model and asks it to answer using only that information, ideally citing the sources.
The model is not retrained on your data. Your documents stay in your own store, access can be controlled per user, and updating the knowledge is as simple as updating the documents.
Where RAG works well
- Internal help desks: HR policies, IT procedures, onboarding guides
- Customer support: product manuals, FAQs, warranty and returns rules
- Sales and pre-sales: proposals, case notes, technical specifications
- Compliance and operations: standard operating procedures and checklists
The common thread is a large body of text that people search repeatedly, where a quick, sourced answer saves real time.
What it does not solve on its own
RAG is only as good as the content behind it. Outdated, contradictory or poorly structured documents produce poor answers. It is also not a replacement for calculations, live transactions or decisions that need human judgement — those need integrations, rules or a person in the loop.
How a RAG project typically runs
- Choose one high-value use case and define what a good answer looks like.
- Collect and clean the source documents, and decide who may see what.
- Build a pilot with search, the language model and source citations.
- Test it against a set of real questions and measure accuracy.
- Add monitoring, feedback buttons and a content-update process, then roll out.
Keeping data safe
Use providers and settings that do not train public models on your data, encrypt documents at rest and in transit, and apply the same permissions in the assistant as in your document systems. Log questions and answers so you can audit quality and spot gaps.
Measuring success
Agree measurable goals before building: the share of questions answered correctly, time saved per query, reduction in support tickets or faster onboarding. A small evaluation set of real questions with approved answers is the most useful tool for tracking quality as the system evolves.
Our AI and machine learning team builds RAG assistants and evaluates them against your own questions. If you want to learn to build them yourself, see our AI & Applied Machine Learning program.