AI Evaluation Record

Use with AI Evaluation and Reliability. Define criteria before running cases. The blank record covers one version comparison; example numbers describe neither model performance nor universal release thresholds.

Copyable record

AI Evaluation — Date / Task

Scope

• User and intended action:
• Input materials, permissions, and de-identification:
• Manual baseline and total time:
• Unsupported tasks:

Test setup (fixed before running)

• Current version / candidate:
• Model, prompt, retrieved materials, and tool versions:
• Run date, region, memory settings, and service versions that cannot be pinned:
• The one main change:
• Development cases / held-out cases not used for tuning:
• Attempts per case and time, call-count, and spending limits:
• Conditions that must all pass:
• Quality comparisons and human scoring rubric:
• Reviewers / disagreement process:
• Relevant failures: absent sources / prompt injection / unauthorized actions / unbounded retries or duplicate side effects / transcription or image errors:
• Long-running fields: task/run IDs, checkpoints, wall-clock time, timeout, retry limit, idempotency key, and human takeover point:
• Isolation fields: clean environment, network egress allowlist, credential scope, and untrusted browser/page-input note:
• Policy fields: provider policy name, version, effective date, region, and high-risk human review:

Case records (copy this section separately for each version)

• Version for this section:
• Use case ID / trial number, such as 07 / 2; link inputs to fixed case materials.

Case (copy once per attempt)

• Case ID / trial number:
• Case type and fixed input location:
• Expected behavior / judging rationale:
• Supporting passage / audio or video timestamp / image region:
• Actual behavior / artifact location:
• Transcript / tool calls / authorization record location:
• Checkpoint / recovery ID / retry and idempotency record:
• Network egress, page or screenshot evidence, and credential scope:
• Final environment outcome (file, database, or user-visible state):
• First-pass acceptance / mandatory failures:
• Human revision minutes / changes:
• Final state (retain failures and timeouts):

Results (calculate per version; retain failures and timeouts in denominators)

• First-pass accepted / all attempts:
• Mandatory failures and specific cases:
• Accepted after human revision / all attempts:
• Total minutes for preparation, execution, checking, and rework:
• Tool charges and the time valuation used:
• Total batch process cost / final accepted count:
• Evidence limits and unsupported inferences:

Decision and follow-up

• Adopt / retain current version / repair and retest / stop:
• Cases supporting the decision:
• New regression cases and rerun scope:
• Retest triggers after model, material, tool, or memory changes; last accepted version:
• Permitted use / human takeover / withdrawal conditions:
• Exit rehearsal after switching providers, disabling memory, or disconnecting tools:
• Owner, review date, and record location:

If no results are accepted, mark cost per accepted result “not calculable.” Preserve total incurred cost rather than replacing it with zero.

Worked example: public-information comparison

This hypothetical case matches the chapter: ten cases with one attempt each. A passes eight on first delivery, B passes nine, and both reach ten after human revision.

Field	Example entry
Change	Extraction prompt only; materials and tools remain identical
Mandatory conditions	Consequential figures have sources; missing prices must not be inferred
Failure B-07	Source gives no price; B writes “free”; reviewer corrects it to “not provided”
A costs	120 total minutes valued at RMB 60/hour, plus RMB 6 for tools: RMB 126
B costs	100 total minutes at the same valuation, plus RMB 8 for tools: RMB 108
Decision	Retain current scope; repair B before expanding use
Next action	Add missing, outdated, and conflicting price cases; rerun previously passing cases
Evidence limits	Small sample, one attempt per case; no production error-rate inference; repaired correctness does not establish autonomous reliability

Store actual results in inspectable files. A completion record needs an artifact, test output, or system state, beyond the model's claim of success.

Turn the record into action

Fix one main error pattern per round and check for regressions. If the process remains unclear, revisit the AI Task Brief. Before offering the workflow as a service, use a Startup Experiment to test demand separately from output quality.

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Source: Life Level-up Guide · Manuscript updated: 2026-10-16. Content: CC BY-NC 4.0.