Apr 15
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When an AI system gives a bad answer, the first question shouldn’t be “which model did we use?” It should be: Was it… – the instruction in the prompt? – the examples? – the fed input data? Because “output quality” is a result of multiple inputs. That’s why context grounding is one of the most useful metrics you can add. Not just “is the answer good?” But: is the answer actually supported by the context we provided? Once you measure that, two useful things happen: You can diagnose where quality breaks – prompt vs. retrieval vs. examples You can improve systems systematically – by changing the right input, not guessing In production, this matters more than people think. You can have a strong model and still ship weak results if the system is poorly grounded – or if you can’t tell whether the context helped or hurt. Better metrics start with better attribution. If you’re evaluating LLM or RAG outputs today – what’s hardest: separating prompt issues from retrieval issues, or defining metrics your stakeholders trust? #AI #LLM #RAG #Evaluation #MLOps #EnterpriseAI #AIEngineering