The PM's Complete Guide to AI Evaluation Frameworks
Why Evaluation Is the PM's Job
In most AI teams, evaluation falls into a gap between engineering and product. ML engineers evaluate models against academic benchmarks. Product designers evaluate UX flows against usability heuristics. Nobody evaluates whether the AI feature, end to end, actually solves the user's problem in a way they trust. That gap is yours to fill.
This is not optional work. Without a clear evaluation framework, your team makes launch decisions based on vibes. "The model seems better" is not a shipping criterion. Neither is "our BLEU score went up 3 points" if users cannot tell the difference. As the PM, you own the definition of quality for your product. In AI, that definition must be explicit, measurable, and testable.
Companies that get this right ship faster. Airbnb's AI team reported that investing in evaluation infrastructure cut their iteration cycle from 3 weeks to 4 days, because the team could assess changes without waiting for manual review of every output. Evaluation is not overhead. It is the single biggest accelerant for AI product development.
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