A/B Model Test Demo

Choose one intent, evaluate candidates A/B/C/D with common decision metrics, and select a winner.

A/B Model TestEnterprise demoDummy data

Support Assistant candidates A/B/C/D on shared release-gate dataset slices.

Selected Eval Intent

Release Gate

Compared Candidates

A / B / C / D

Winning Candidate

Decision Confidence

96%

Intent-Locked Candidate Comparison

Side-by-side results for candidates using the same intent and metric set.

Intent: Release Gate

Candidate	Agent	Dataset	Decision	Critical Failure Rate	Pass Rate
A	Support Assistant	Regulated Support v4	Pending	3.1%	90.4%
B	Support Assistant	Regulated Support v4	Ship	1.3%	95.1%
C	Concierge Agent	Regulated Support v4	Ship	2.0%	93.8%
D	Voice QA Agent	Regulated Support v4	No Ship	4.8%	86.7%

Baseline vs Candidate Trend

Dummy trend data for this eval intent.

Evaluator Score Split

LLM, human, and reviewer scoring spread across derived metrics.

Sample Findings

Representative row-level dummy findings for this eval type.

ab_203 · Helpfulness

improved

Baseline 0.61 to Candidate 0.89

Candidate B response was both shorter and more complete.

ab_224 · Coherence

neutral

Baseline 0.82 to Candidate 0.81

No meaningful difference in simple FAQ prompts.

Enterprise Considerations

Governance expectations for enterprise adoption of this eval type.

A/B winner promotion requires minimum confidence threshold >= 95%.

Segment-level fairness checks are mandatory before decision.

Decision memo is archived with baseline/candidate artifacts.