How to turn production traces into better AI with Currai

The fastest way to improve an AI product is to stop guessing and start from what the product already did.

Production traces are the raw material. They show real user inputs, model outputs, prompt versions, tool calls, retrieval context, latency, token usage, cost, and errors. Currai turns that record into an improvement loop: inspect, evaluate, fix, measure, repeat.

Step 1: review real conversations

Start with a slice of recent traces from one important workflow. Do not try to audit the whole product at once. Pick a support assistant, onboarding flow, RAG answer, agent task, or any prompt that users rely on.

Open traces and read them like a user would. Look for repeated product failures:

• answers that are generic when policy details are required
• tool calls that happen too late or too often
• retrieval context that is ignored
• escalations that never happen
• multi-turn conversations that lose context

The goal is not to label everything. The goal is to find a pattern worth fixing.

Step 2: define the eval

Once the pattern is clear, turn it into an eval rubric. Keep the first rubric narrow and concrete.

For a refund support failure, a good response might need the refund window, eligibility requirements, next steps, and review timeline. For a grounded answer failure, the rubric might check whether every factual claim is supported by retrieved context. For an agent workflow, the rubric might check whether the agent completed the task without unnecessary steps.

Currai evals work best when the rubric maps to a decision the team can act on.

Step 3: improve the prompt or product flow

The fix might be a prompt change, but it is not always a prompt change.

Sometimes the answer fails because the policy is missing from context. Sometimes the agent needs a tool result before answering. Sometimes the product should route the conversation to a human. Sometimes the model is fine and the workflow around it is brittle.

Currai helps here because the trace shows more than the final response. You can see which step caused the failure.

Step 4: rerun and compare

After the change ships, run the same eval on comparable traces. Compare prompt versions, model versions, experiment arms, or time windows.

The question is simple: did quality improve on real behavior?

If the score improves without unacceptable cost or latency tradeoffs, promote the change. If it regresses, roll back or narrow the fix. If the result is mixed, inspect failing traces and refine the rubric.

Step 5: keep the failure as a regression

The best evals become durable checks. A failure that happened once can happen again after a prompt edit, model upgrade, retrieval change, or new product workflow.

Save the trace pattern. Keep the rubric. Reuse it when the team changes the system.

That is how Currai turns production traces into better AI: not by creating a static benchmark, but by making real product behavior measurable.

Related: Prompt A/B testing, LLM evals, and Run LLM evals on production traces.