RAG
Words made simple
A few useful meanings before you read the explanation.
- RAG
- Find relevant information first, then give it to a model to help answer the question. The answer is only as reliable as the information and checks used.
- Retrieval
- Finding and fetching information that is useful for the current question.
- Evaluation
- Checking results against examples and clear criteria to find whether the system works well and where it fails.
Beginner — What is RAG?
- RAG retrieves relevant external evidence and gives it to a model before answering.
- It lets the insurance assistant use current private policy documents and produce source-backed explanations instead of relying only on learned knowledge.
Intermediate — RAG or long context?
- If the task already identifies a small permitted document set, long context may be simpler.
- For a large changing corpus, retrieval selects relevant evidence more efficiently.
- Both need access control, applicability checks, and answer evaluation.
Senior — How do you debug wrong RAG answers?
- I inspect parsing, document version and permissions, chunk boundaries, candidate recall, ranking, supplied context, and answer support in that order.
- I score retrieval and generation separately, then improve the failing stage rather than automatically using a larger model.
Scenario — An answer cites a real document but is wrong. Why?
- The cited document may be irrelevant, expired, inapplicable, or missing an exception; the answer may also misinterpret it.
- I validate source eligibility and claim support, not just whether the link exists.
Can you explain it without the notes?
RAG
Use your own words. Explain an example and one limitation before checking the explanation.
Check your explanation
Beginner — What is RAG?
Turn this concept into a clear answer.
Practice this conceptAdapted from 13_AI.MD · “29.3 RAG”. Examples are study fragments; framework behavior can depend on your version.