Evals for PMs: a practical guide to AI product quality with Currai

AI product quality is not only an engineering problem. The hard questions are usually product questions: did the assistant understand the user's intent, follow policy, choose the right next step, and create an experience the team is willing to ship?

That is why PMs should be close to evals.

An eval is useful when it turns product judgment into a repeatable check. It does not replace engineering tests, human review, or support expertise. It gives the team a shared way to measure whether the AI system is getting better.

Start with production traces

The best PM eval work starts by reading real interactions. In Currai, production traces show the user message, model response, prompt version, retrieved context, tool calls, latency, cost, and metadata in one place.

That context matters. A final answer can look polite and still fail the product. A support assistant might tell a customer to "contact support" when the policy requires a clear refund window, eligibility requirements, and next steps. A sales assistant might answer a pricing question without checking the current plan. A workflow agent might complete successfully but skip a required handoff.

Those are not abstract model failures. They are product failures.

Turn judgment into rubrics

Once a recurring pattern appears, write the rubric in language the team can defend. Good rubrics are narrow:

• Did the answer include the required policy details?
• Did the assistant escalate when the user needed a human?
• Did the response use only retrieved context?
• Did the agent complete the user's task without unnecessary tool calls?

Avoid broad rubrics like "overall quality" until the team has smaller checks it trusts. Narrow rubrics create clearer failures and make disagreements easier to resolve.

Use Currai as the loop

Currai connects the pieces PMs need: production traces, prompt versions, model outputs, and eval results. That means a PM can review real failures, define what good should look like, run an eval, and compare results after a prompt or model change.

The loop is simple:

1. Inspect traces from real users.
2. Find a recurring failure.
3. Create an eval rubric.
4. Improve the prompt or product behavior.
5. Rerun the eval on comparable traces.

That is AI product quality as an operating loop, not a one-time launch review.

PMs make evals useful

Engineers make evals reliable. PMs make evals relevant.

The strongest AI teams bring both together. Engineering ensures the traces are complete and the eval runs are reproducible. Product ensures the criteria match the user experience and business risk.

If you are starting from scratch, pick one high-risk workflow and one prompt. Read twenty traces. Write down the most common failure. Turn that failure into a rubric. Then use Currai to measure whether the next change actually improved the product.

Related: LLM evals, Prompt management, and Run LLM evals on production traces.