A generative-AI demo is easy to put together. Turning it into an AI product people rely on is the hard part, and what separates the two is measurement. If you can't put a number on how good the output is, you've got no way to tell whether your last change helped or quietly broke something.
Start with the eval set, not the prompt
Before you write a single prompt, collect 30–50 real cases: the actual questions, documents, and edge cases your users will throw at it. For each one, write down what a good answer looks like. That's your eval set. It'll be the most valuable thing you build on the whole project.
Measure, then move
Once the eval set exists, every change you make is accountable:
- Swap the model? Re-run the evals.
- Change how you retrieve context? Re-run the evals.
- Tighten a guardrail, or reword a prompt? Re-run them again.
Now you're not arguing about whether the AI "feels" better. You're watching a score move.
Guardrails are part of the product
People trust the output when answers are grounded in your own data (RAG) and come with citations. Agents that take actions need the same kind of control: tool permissions, dry-run modes, and a human-in-the-loop at the exact step where a mistake would be expensive.
Cost is a design constraint
Token cost adds up fast. We model the spend per workflow, cache the parts that don't change, and route each step to the smallest model that still passes the evals. Get that right and your AI bill tracks the value the work produces, instead of ballooning on wasted calls.
Treat AI like software. Test it, watch it in production, and ship when the eval score clears the bar you set for it.