AI Evaluation

Build a balanced evaluation system that measures outputs, catches structural errors, applies expert judgment, and learns from the people the work is meant to help.

EVALUATE FROM MULTIPLE ANGLES

No single eval can explain quality on its own. Use production traces to find failure patterns, golden datasets to preserve what good looks like, code assertions to enforce structure, LLM judges for nuance, and client feedback to validate real-world value.

01 · REGRESSION COVERAGE

Golden Dataset

List expected input–output pairs, rerun every case after a change, and compare the result to what good looks like. Curate new cases from production failure analysis.

Compare desired outputs

02 · STRUCTURAL VALIDITY

Code Assertion

Use deterministic checks for exact source quotes, red-flag wording, node distributions, thresholds, and valid JSON before an output reaches the next step.

Check structure automatically

03 · QUALITATIVE REVIEW

LLM-as-a-Judge

Send an output and clear evaluation criteria to multiple judge models. Compare verdicts and rationale when quality requires context and human-like judgment.

Compare independent verdicts

04 · OUTCOME QUALITY

Client Feedback

Ask customers whether the response actually worked for them. Their experience tests the limits of every definition of correctness set by an assertion or judge.

Capture the real outcome

Start a Golden Dataset

Begin with the expected input–output pairs. Each case becomes a repeatable regression check as your product changes.

Evaluation Type

Dataset Name

Candidate Input

Expected Output

GOLDEN DATASET PLAYBOOK

Curate what good looks like.

1. Start with the highest-value inputs. 2. Run each case after every change. 3. Investigate failures in production traces. 4. Add confirmed failures back into the dataset.

CLIENT FEEDBACK

Did the response actually work?

Client feedback is the outcome layer: it tests whether the AI response helped someone complete the work, not just whether it matched a predefined definition of correctness.