Final artifact
One scoring rubric, one grader spec, and one calibration sheet.
Assessment
Rubric grading lab: turn 'looks better' into reviewable scoring
This audit forces you to build a scoring rubric, grader spec, and calibration sheet for one live workflow. DepthPilot cares less about a pretty scorecard and more about whether you can explain which dimension failed, why it failed, and what to fix first.
Real acceptance criteria
Not that a score exists, but that a second reviewer can reuse the logic and severe failures cannot hide inside an average.
Where our value shows
This page turns eval from taste and opinion into a mechanism for diagnosis, prioritization, and rollback decisions.
DepthPilot Summary
This lesson teaches how to turn 'looks better' into reviewable quality
Many teams know they need evals but still make decisions on taste. DepthPilot focuses on dimension design, anchor writing, hard-fail rules, and calibration. After this lesson, the user should be able to explain whether a score dropped because of factuality, instruction following, citation quality, or escalation judgment instead of saying the output just feels worse.
Break quality into dimensions first
- Without dimensions, you only have opinion and cannot locate failure.
- Every dimension needs clear anchors or reviewer scores are not comparable.
- A useful rubric never averages severe errors into invisibility.
Define grader behavior next
- The grader must specify what evidence it reads, when it checks traces, and when it overrides.
- Human review and automated grading should share one rule system instead of two standards.
- Uncertain cases need explicit grading rules in the spec.
Calibrate before you trust scores
- Reviewer disagreement is not noise. It is evidence the rubric needs work.
- Scores without calibration rarely support launch, rollback, or repair order.
- The deliverable is a reusable quality mechanism, not a decorative spreadsheet.
Scoring rubric
- Define dimensions before total score so quality is not reduced to one impression.
- Write 0-3 anchors for each dimension so reviewers do not improvise meaning.
- State which dimensions can trigger a hard fail instead of being averaged away.
- Make the rubric point back to actual repair layers.
Grader spec
- Write what evidence the grader inspects, how it scores, and when it must reference the trace.
- Separate final-answer grading from trace grading.
- Make the grader spec usable by both humans and automated graders.
- Write rules for uncertain cases instead of leaving room for grader vibes.
Calibration sheet
- Keep reviewer disagreements visible instead of jumping to one average.
- Use disagreement patterns to improve rubric anchors.
- Treat calibration as quality governance, not as a one-time ritual.
- Make it directly useful for launch, rollback, and repair order.
Proof you must keep before launch
One scoring rubric with dimensions, anchors, and hard-stop rules.
One grader spec that defines inputs, scoring steps, and override conditions.
One calibration sheet that records disagreement and revision notes.
One short recap explaining whether factuality, citation, instruction following, or escalation judgment should be fixed first.
Reusable grading templates
Download the scoring rubric
Define dimensions, anchors, and hard-stop conditions.
Download the scoring rubricDownload the grader spec
Write what the grader inspects, how it scores, and when it overrides.
Download the grader specDownload the calibration sheet
Capture reviewer disagreement and refine the rubric.
Download the calibration sheetReference appendix
These links anchor rubric and grader design principles. The actual lesson is the scoring dimensions, rules, and calibration process above.
Search Cluster
Connect rubric design to discoverable eval topics
High-intent users often enter through eval, observability, or rubric searches before realizing the deeper problem is dimension-based scoring and calibration.
LLM Evaluation Rubric
An LLM evaluation rubric is not scorecard theater. It drives repair order and launch decisions.
Many people searching for an LLM evaluation rubric only want a template. DepthPilot goes further: we turn rubric design into dimensions, anchors, hard-stop rules, and grader instructions that help you decide what broke and what to fix first.
Open pathAI Eval Loop
AI eval loops decide whether you are improving a system or just guessing
Serious AI products do not treat 'it feels better' as evaluation. Users who search for AI eval loops usually already sense that prompt and workflow improvements will not compound without real measurement.
Open pathLLM Observability Guide
An LLM observability guide focused on replayable failures, not just more logs
Many users search for LLM observability because the system broke and they do not know how to inspect it. DepthPilot focuses on something stricter: recording traces, labeling failures, and replaying bad runs so debugging becomes systematic.
Open path