Why your traces and evals belong in the same place

Most AI teams start with separate systems. Logs live in one place, traces in another, prompt versions in a dashboard, eval datasets in spreadsheets, and judge results in a notebook.

That works until the team needs to answer the only question that matters: did the product get better?

To answer that, traces and evals need to share the same evidence.

Traces show the real run

A production trace is the record of what actually happened. It includes the input, output, prompt version, model, tool calls, retrieval spans, latency, tokens, cost, user, session, and environment.

That context is hard to reconstruct later. If an eval only sees a prompt and an answer, it may miss why the answer failed. Maybe retrieval returned the wrong document. Maybe a tool timed out. Maybe the prompt version was part of an A/B test. Maybe the failure only makes sense in the previous turn of the session.

Currai keeps those details connected, so the eval can judge the actual behavior the user saw.

Evals make traces actionable

Trace review is high signal, but manual review does not scale. Evals turn trace patterns into repeatable checks.

For example, a support team might notice that refund answers are vague. The trace shows the failure. The eval turns it into a product check:

• Did the response include the refund window?
• Did it explain eligibility?
• Did it ask for the order email, order ID, and reason?
• Did it mention the review timeline?

Now the team can score many traced answers, compare prompt versions, and decide whether the fix worked.

One dataset, many decisions

When traces and evals are separated, teams spend time stitching together data before they can make a decision. When they live together, the same production evidence can answer multiple questions:

• Which prompt version performed better?
• Did quality improve without increasing cost?
• Are failures concentrated in one user segment?
• Did a model change reduce latency but hurt policy compliance?
• Which traces should become regression cases?

This is the practical advantage of Currai. Observability is not just for debugging after something breaks. It becomes the source of truth for continuous evaluation.

The operating model

The workflow is straightforward:

1. Instrument the application so important model calls are generations and supporting work is captured as spans.
2. Attach prompt names and prompt versions to generations.
3. Run evals against traced outputs.
4. Compare results by prompt version, model, route, user segment, or time.
5. Open failing traces and fix the underlying behavior.

The result is a tighter loop. Teams stop arguing about whether a prompt "feels" better and start comparing how it performed on the same product surface.

Related: LLM observability, LLM evals, and Active observability for LLM apps.