Case Study
Value Discovery & Portfolio
Prioritize GenAI investments with defensible stop criteria and risk-aware gating, using resources on initiatives that can scale.
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Responsible AICase Study
Technical Assurance
Use evaluation ladders and release gates for GenAI reliability and safety, preventing quality drift as systems evolve.
Executive Outcome
01
Automated evaluations that detect regressions before release, reducing reliance on production discovery.
02
Scorecards that standardize go/no-go decisions and replace subjective 'eyeball checks' with measurable criteria.
03
Sustained reliability over time through versioning, traceability, and rollback discipline as models, prompts, tools, and data evolve.
Engagement focus
Evaluation ladder and release gate discipline for GenAI reliability.
Context
GenAI behavior changes with model updates, prompts, tools, and data drift. The goal was to treat GenAI changes with the same rigor as code changes, using staged evaluation, measurable gates, and operational readiness for rollback and incident handling.
The Challenge
- 01Prompt, model, or data updates introduced silent regressions in quality and safety.
- 02Release decisions were driven by subjective review and inconsistent sampling.
- 03There was no scalable way to quantify the impact of changes across representative scenarios.
- 04Rollbacks were difficult without disciplined versioning and evidence of what changed.
Approach
- →Defined an evaluation ladder spanning offline evaluation, staged rollout signals, and production monitoring for early regression detection.
- →Established automated scorecards across quality, safety, latency, and cost to standardize decision-making.
- →Introduced release gates with explicit pass/fail criteria, aligned to risk tier and operating context.
- →Standardized evaluation packs and change logs to support repeatability, traceability, and rollback readiness.
Key Considerations
- Maintaining evaluation datasets requires ongoing investment, curation, and ownership.
- Running evaluations at meaningful coverage has compute and time costs that must be budgeted.
- Stricter gates can slow releases in exchange for reduced incident risk and higher confidence.
Alternatives Considered
- ✕Production-only testing: rejected due to safety, reputational, and compliance risk exposure.
- ✕Manual QA only: rejected because it does not scale to non-determinism and variance across contexts.
Representative Artifacts
01Evaluation ladder definition (stages, signals, and gates)
02Scorecard template (metrics, weights, and fail conditions)
03Release gate criteria by risk tier and change class
04Rollback criteria and operating procedures
05Change log specification for prompts, tools, and policies
Acceptance Criteria
No change is promoted without passing the applicable scorecard and gate criteria.
Monitoring signals trigger defined escalation and rollback paths when degradation is detected.
Regression suites execute automatically as part of the delivery workflow.
Metrics and decision records are persistent and traceable across versions.
Validation Datasets
| Dataset | Kind | Target |
|---|---|---|
| RAG Grounding & Citation Pack | golden | Measure faithfulness/grounding and citation coverage. |
| Policy Compliance & Refusal Pack | regression | Verify refusal behavior for restricted intents. |
| Tool-Use Correctness Pack | golden | Validate tool selection and argument correctness. |
| Adversarial Prompt Injection Pack | adversarial | Detect susceptibility to injection and jailbreak attempts. |
| Voice Interaction Pack | voice | Evaluate call flows, intent detection, and safety. |
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