What Is RAG, Without the Buzzwords
A non-technical explanation of how AI uses your documents to give grounded answers, with citations. No jargon required.
RAG, without the buzzwords
RAG stands for Retrieval Augmented Generation. It is one of those acronyms the industry uses to make a simple idea sound technical. The simple idea is this: before the AI answers, look up the relevant parts of your own documents and let it use those instead of guessing.
A model on its own knows what was in its training data. It does not know your employee handbook, your customer contracts, or the policy your CFO wrote last quarter. RAG is the bridge between general knowledge and your knowledge.
The reason this matters: an answer with a citation is something you can verify. An answer without one is something you have to trust. For most enterprise use cases, the difference is the difference between useful and unsafe.
How it works, in three steps
Step one: index your documents
Take the documents that matter (handbooks, policies, contracts, product specs, internal wikis), and turn them into a format the AI can search through. This happens once, then runs in the background as new documents come in. The output is a private library that lives inside your environment.
Step two: find what is relevant
When somebody asks a question, the system searches the library for the few passages most likely to contain the answer. It does this with meaning, not keywords, so a question about "remote work" finds policies that talk about "telework" or "working from home". The relevant passages are passed to the model as context.
Step three: cite the source
The model answers using the passages it was given, and shows where each statement came from. The user gets the answer plus a link to the original document. If something looks off, they can verify in one click. No black box.
What changes for the user
Same question, two assistants. The first works with general training data. The second works with your knowledge base.
"How many days of paternity leave do I get?"
Generic answer based on what the model was trained on. Maybe accurate for some country in some year. Useless for an actual decision.
No source. No way to verify. No update path when policy changes.
"How many days of paternity leave do I get?"
Specific answer pulled from the current employee handbook, with a citation. If the policy changes, the next answer reflects it automatically.
Source: Employee Handbook v2026.1, section 4.3
What can go in a knowledge base
The exact list depends on the platform. The Plainsight assistant handles the formats you have lying around in most organisations, without conversion projects.
Related articles
Ready to see it in action?
Schedule a personalised demo and see how the Plainsight AI Assistant fits your organisation.