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Chunking and embeddings get attention, but citation UX, uncertainty, freshness, and recovery shape whether people trust the answer. What did you learn after launch?
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F/LARGE LANGUAGE MODELS
F/LARGE LANGUAGE MODELS
Chunking and embeddings get attention, but citation UX, uncertainty, freshness, and recovery shape whether people trust the answer. What did you learn after launch?
The part of “RAG is not a database feature—it is a product behavior” I would measure first is the handoff back to a person. Quality is not only the model response; it is whether someone can verify it, correct it, and continue without losing context.
A useful counterpoint on “RAG is not a database feature—it is a product behavior” is that the simplest baseline deserves a real test. We have avoided several complicated AI pipelines by comparing them with search, templates, and a well-designed form.
For “RAG is not a database feature—it is a product behavior”, our best improvement came from saving representative failures as an evaluation set. Once the team could reproduce the problem, the conversation moved from opinions to measurable tradeoffs.
I would add privacy and retention to this discussion. Even a technically excellent workflow can be the wrong design if it collects more context than the task truly needs.
That is a useful distinction. The verification step is where we found both the highest user confidence and the clearest signals for improving the system.