What are AI Evals?
AI Evals combine datasets, metrics and review methods to measure a generative system under defined scenarios.
A useful eval records input, expected behavior, scoring method and system version so changes remain comparable.
Why is AI evaluation necessary?
Fluent output is not necessarily correct or safe. A model, Context or tool change can improve one scenario and regress another.
Evaluation exposes regressions before release and measures real behavior afterward.
What should we evaluate?
Metrics should come from the business outcome and scenario risk.
- Correctness and relevance
- Faithfulness and citations
- Task success and tool selection
- Safety and policy compliance
- Latency and cost
Response and agent evaluation
Final Response Evaluation measures the final output. Trajectory Evaluation measures the path and sequence of tool calls.
Google Cloud documents both categories because a correct answer can still come from an unsafe, wasteful or accidental path.
Dataset and Ground Truth
Datasets should cover normal, boundary, difficult and invalid inputs. Ground Truth can be a reference answer, expected tool, policy or successful outcome.
Evaluation data should be versioned and separate from demonstrations or training data.
What is LLM-as-a-Judge?
A judge model scores output against a rubric. It scales review but needs calibration against human examples and checks for bias and instability.
Deterministic criteria such as JSON validity or citation presence should use code-based checks where possible.
Evaluation before and after release
Pre-release evals compare versions against acceptance thresholds. Production feedback, failures and sampled traces then expand the dataset.
Sensitive real-world data must be minimized, redacted and retained under explicit rules.
Evals in the enterprise
Assign an owner, success metric, stop threshold and review cycle to every scenario. Cost per successful task and human escalation can matter more than a broad model score.
Grounded answers also need evaluation of whether citations actually support claims.
Frequently asked questions
Are manual tests enough?
No. They help exploration, but version comparisons need repeatable data and metrics.
Does LLM-as-a-Judge replace people?
No. It must be calibrated and high-risk decisions still need review.
Is the final answer enough for an agent?
No. Tool trajectory, steps, errors and policy compliance also matter.
Sources
- Google CloudAgent evaluation30 July 2026 · Official Documentation
- Google CloudGen AI evaluation service overview19 August 2026 · Official Documentation
- Google CloudEvaluate Gen AI agents21 August 2026 · Official Documentation