A proof of concept is a minimal implementation designed to answer one question: does this approach actually work for this problem? In AI, that means: does the model produce useful output on representative real data, and is the quality gap between "current state" and "what the model produces" actually valuable?
Good POCs are time-boxed (2-4 weeks), use real data (not curated samples), define a specific success criterion before building ("80% of outputs require no edit"), and involve the end users who'll actually use the result. Bad POCs are "demos" that look impressive on cherry-picked inputs but fall apart on real usage.
The most important output of a POC isn't the code — it's the measurement. If you can't quantify quality improvement, you can't make a business case for full deployment. Build the eval harness as part of the POC.
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Knowing the term is one thing. Shipping it is another.
We do two-week AI Sprints — one term, one workflow, into production by Day 10.