Evals and Observability

Evals and Observability

If you cannot measure quality, every prompt change is a guess.

What to evaluate

• Correctness — did it answer the actual question?
• Groundedness — did it stay inside the retrieved context?
• Safety — did it refuse or leak what it should not?
• Latency and cost — is this still shippable?

You do not need a research lab. Start with a golden set: 30–100 real examples with expected answers or rubrics.

What to log on every request

• prompt version
• retrieved chunks / tool calls
• model, temperature, token counts
• output
• user feedback (thumbs, edits, retries)

Without traces, you cannot tell whether a bad answer was retrieval, prompting, or the model.

A practical cadence

1. 1

   Log everything in staging

   Make traces the default, not an afterthought.

2. 2

   Score the golden set

   Run it after each prompt or model change.

3. 3

   Sample production weekly

   Turn repeating failures into new eval cases.

Next

Then ship it like software:

• Shipping AI to Production