How to explain RAG and evals simply (in interviews and at work)

A plain-language way to explain retrieval and testing that shows judgment instead of jargon.

By MT, AI Solutions Engineer. Updated 2026-10-02.

Key takeaways

  • RAG means the AI answers from your documents instead of its memory.
  • Evals mean testing the AI on examples it hasn't seen and measuring how often it's wrong.
  • Explain both as problem, fix and proof, not as definitions.

Start with the problem, not the acronym

AI models can be confidently wrong. Ask one about your company's refund policy and it may invent a plausible answer, because it has never seen your policy. That's the problem RAG solves. Starting there makes everything after it make sense to any listener.

RAG in plain words

RAG stands for retrieval-augmented generation. In plain words: before the AI answers, the system looks up the most relevant passages from your own documents and gives them to the AI, with an instruction to answer only from those passages and to cite them.

A simple way to say it in an interview: "Instead of asking the AI to remember, we hand it the right pages and ask it to answer from them, with sources. If the answer isn't in the pages, it should say so."

How it fails

Naming failure modes is what makes you sound experienced. The common ones:

  • The search pulls the wrong passages, so the AI answers from the wrong source.
  • The right answer is split across documents and only half gets retrieved.
  • The documents are out of date, so the answer is confidently old.
  • The AI ignores the instruction and fills gaps from memory anyway.

Evals in plain words

An eval is a test set: questions with known right answers that you didn't use while building. You run the system on them and count how many it gets right, how many it gets wrong, and how many it correctly refuses.

The most valuable category is the questions whose answers are not in the documents. A good system says "I don't know" and hands off to a person. A bad one makes something up. That's the number people care about.

A 60-second answer you can adapt

"AI models can be confidently wrong, so for our helpdesk tool we used retrieval: the system finds the relevant pages in our documentation and the AI answers only from them, with a citation. To prove it worked, I built a test set of 30 questions, including 8 whose answers weren't in the docs. First run, it answered 4 of those 8 anyway. I tightened the instructions and added a confidence check, and on the rerun it correctly handed off all 8."

Use your own real numbers. The structure, problem then fix then proof, is what makes it land. For the rest of the interview, see the ten questions you'll likely get.