Artificial Intelligence

Retrieval-augmented generation (RAG) is easy to demo and surprisingly hard to evaluate. A fluent answer can hide weak retrieval, while a good retriever can be blamed for an answer model that ignores its evidence. A useful evaluation process separates those failure modes. You do not need thousands of examples to begin. A carefully maintained golden dataset of 30 to 100 representative questions can catch many regressions before users do. Define what the system is supposed to do Start with the product contract rather than a model metric. For a documentation assistant, useful requirements might be:

Large language models are useful when software needs to turn ambiguous text into a structured decision, extraction, or plan. The dangerous shortcut is to treat a model response as if it were already trusted application data. Even when a provider can constrain output to JSON or a schema, the result can still be semantically wrong: a date can be impossible, an identifier can refer to a nonexistent record, or a supposedly positive amount can be negative. Reliable integrations therefore need a contract boundary between model output and the rest of the system.