AI Evaluation Workshop
The difference between an AI that demos well and an AI you can put in front of customers is evaluation. This is the curriculum that closes that gap.
Demos pass. Production does not.
Most teams meet AI through a demo. The demo is curated, the inputs are friendly, and the result is impressive. Then the same system meets real users, real edge cases, and real adversaries, and the gap between the demo and the deployment becomes the whole story. The missing discipline is evaluation: a repeatable way to measure whether a model is good enough, on the cases that actually matter, before it ships and after every change.
The constraint this work answers is not building a model. It is building the judgment around a model: what to measure, what counts as a pass, how to catch the failure that only shows up under pressure, and how to keep that bar steady as the system changes.
Twenty modules from demo to defensible
The workshop is a curriculum of twenty modules. It is not a single eval script. Each module is a decision a team has to settle in order to trust a model in production, sequenced so that the decisions build on each other:
- Evals. What good looks like for this task, written down as something you can score, not a feeling in a meeting.
- Datasets. Building and curating the cases you evaluate against, including the hard ones a demo would never show.
- Guardrails. The checks that sit between the model and the user, so a bad output is caught rather than shipped.
- Red teaming. Actively trying to break the system, because the failure you do not look for is the one that finds your users.
- Quality gates. The bar a change has to clear before it goes live, so quality is enforced by process, not by hope.
The output is a method a team can run on its own work, repeatedly, long after the workshop ends.
Evaluation can be taught, not just admired
It demonstrates that the move from impressive demo to trustworthy deployment is a teachable, reproducible discipline rather than a black art that lives in one engineer's head. The curriculum makes the judgment explicit: the questions to ask, the order to ask them in, and the gates that turn answers into a standard the whole team can hold. The proof here is the method itself, which anyone can read, run, and challenge.
Where the honesty line sits
- This is a reproducible curriculum, not a record of a specific client deployment. It does not claim production outcomes for any named company.
- It teaches the evaluation discipline. It does not certify any particular model as safe or compliant for any particular use.
- The hard work still belongs to the team that runs it. The curriculum is the method, not a substitute for doing the evaluation on your own system.
- Judgment stays human. The gates and red teaming surface evidence, but the decision to ship remains a human call.
Everything is on GitHub
The curriculum is public. You can read the modules, see how the decisions are sequenced, and use or adapt the material for your own team.
Most client work stays private by default. This curriculum is the part of the method that can be shown, read, and pressure tested by anyone. Start a conversation →