> ## Documentation Index
> Fetch the complete documentation index at: https://docs.platform.nora.my/llms.txt
> Use this file to discover all available pages before exploring further.

# Before/after

> Side-by-side comparison — did the change actually improve things?

Simulation shows how the target did. **Before/after** shows how it did *compared to the baseline*. The number that matters.

## The comparison view

For every dataset example:

* **Left**: baseline output.
* **Center**: target output.
* **Right**: expected output.
* **Verdict**: `improvement` / `regression` / `same` / `both failed`.

Sort by verdict:

* **Improvements first** — where the target beat the baseline.
* **Regressions first** — where the target broke something the baseline handled.
* **Diff score** — biggest change in either direction.

## Summary metrics

At the top:

* **Pass rate delta** — target's pass rate minus baseline's.
* **Improvements** — count of "target beat baseline".
* **Regressions** — count of "target worse than baseline".
* **Wash** — count of "same verdict".
* **Cost delta** — is the target more or less expensive?
* **Latency delta** — is the target slower or faster?

Big improvement, small regression, minor cost increase → likely ship it. Big regression → don't.

## The regression drill-down

Regressions are the deal-breakers. Click **View regressions** to see just those examples.

For each regression:

* What the baseline said.
* What the target now says (worse).
* Why it changed — Nora's automated diff of the trace shows what step diverged.

Common causes:

* New prompt is more terse and drops a required disclaimer.
* New retrieval preset drops a source the baseline relied on.
* Model swap changes an edge behavior.

## The improvement drill-down

Similar view for improvements. Understand *why* the target won so you can generalize:

* Was it a specific pattern that the change targeted?
* Or did it broadly lift many examples?

Broad improvements are safer to ship than narrow ones.

## Statistical significance

For datasets with \< 50 examples, Nora shows a "high variance — proceed with caution" flag. Small samples make it easy to see noise as signal.

For > 50 examples, a proper significance test is shown: p-value for pass-rate delta, confidence interval. Only trust deltas outside the noise floor.

## Attributed changes

The improvement flow tags every simulation with the specific proposal that generated the target. Before/after inherits that tag, so you can trace:

> "Improvement #42 (retrieval preset change): +8% pass rate, +1 regression, +0.02s latency"

Great for building an audit trail of what changed and why.

## Multi-target comparison

Compare more than two versions at once. Add up to 5 targets to a comparison — the view expands to columns.

Useful for comparing three model choices, or three prompt variants, side-by-side.

## Sharing a comparison

Every simulation comparison has a permalink. Share with reviewers before shipping. They see the same view — no data extraction needed.

Approvers can approve the change directly from the comparison view (see [Approve](/reliable/optimization/approve)).