Evaluation specification
Dimensions, severity, case taxonomy, metrics, thresholds, sampling, and decision policy.
AI evaluation and governanceTurn AI quality into a release criterion.
We design evaluation systems for LLMs, RAG, and agents. Real cases, dimension-level metrics, adversarial testing, and observability converge into explicit gates to promote, hold, or roll back a version.
Behavioral criteriaQuality defined by dimension.
Representative casesReal, synthetic, and adversarial.
Validated judgesAgreement, bias, and failure modes.
Release gatesPromote, hold, or roll back.
Coverage
Evaluation across the whole system.
A final answer can look correct even when retrieval, tool selection, permissions, or state failed. Evaluation must follow the layers where behavior is produced.
Quality and reliability
- Correctness, relevance, and completeness
- RAG retrieval and groundedness
- Tool choice and argument quality
- Agent trajectory and final outcome
- Cost, latency, and recoverable failures
Safety and governance
- Prompt injection and data leakage
- Permissions and high-impact actions
- Bias and segment-level performance
- Policy and compliance controls
- Audit trails and incident response
Controls
Evidence that supports a release decision.
The system combines deterministic checks, semantic evaluation, human review, and production signals according to risk.
Case bank
Representative, edge, regression, and adversarial cases with versioning, labels, and provenance.
Judges and checks
Deterministic validators, calibrated LLM judges, guardrails, and agreement analysis against human labels.
CI and production gates
Automated execution, scorecards, traces, alerts, and explicit promote, hold, or rollback decisions.
Learning loop
The factory learns from every failure.
Evaluation does not end with the initial benchmark. The system must convert production evidence into future coverage.
Observe
Capture inputs, outputs, tools, latency, cost, and outcome signals without retaining unnecessary data.
Classify
Group failures, measure severity, identify gaps, and select cases that represent recurring risk.
Encode
Turn those cases into reproducible checks, judges, and decision policies.
Control
Run them in CI and production, block regressions, and update the bank as the system or use changes.
Frequently asked questions
Evaluation scope
Is an LLM-based judge reliable?
It can be useful for semantic judgment, but it must be validated against labeled examples and combined with deterministic checks whenever possible.
Do you use recognized security references?
The scope can map risks and controls to relevant public references, such as OWASP guidance for LLM applications, without treating a generic checklist as a substitute for analysis of the real system.
Do we need a large production dataset to begin?
No. An initial bank can be assembled from specifications, available logs, adversarial tests, and expert review. Production evidence later expands and recalibrates it.
Which failure cannot ship?
Describe the system, its users, the actions it takes, and the impact of an incorrect response. The assessment begins with the risks that deserve to become gates.