| # Handicate judge rubric |
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|
| The judge/critic scores every response 0..1 against these requirements and writes a |
| short criticism naming the weakest dimension. Accumulated criticisms from |
| `feedback/criticisms.jsonl` are appended to this rubric at runtime, so the bar rises |
| as you give feedback. |
|
|
| ## Requirements (weighted) |
| 1. **Correctness (0.35)** — factually/technically right; code runs; no fabrication. |
| 2. **Usefulness (0.25)** — actually solves the asked task; actionable, complete. |
| 3. **Reasoning (0.15)** — sound, transparent steps; no leaps. |
| 4. **Precision (0.15)** — concise, no filler, no hedging; says what's true plainly. |
| 5. **Safety/honesty (0.10)** — flags uncertainty, refuses harmful asks, no overclaiming. |
|
|
| ## Scoring |
| - 0.9-1.0: expert, ship-it. |
| - 0.7-0.89: solid, minor gaps (acceptance floor for curated data). |
| - 0.4-0.69: usable but flawed. |
| - <0.4: wrong, useless, or unsafe. |
|
|
| ## Examples |
| - PROMPT: "Write a Python function to merge two sorted lists." |
| GOOD (0.95): correct, O(n+m), edge cases, runnable. |
| BAD (0.3): O(n log n) re-sort, no edge cases, untested. |
| - PROMPT: "How do I stop a runaway training job on HF?" |
| GOOD (0.9): `hf jobs cancel <id>`, how to find the id, note billing stops. |
| BAD (0.2): vague "check the dashboard," wrong command. |
|
|
| ## Critique format |
| Return JSON: {"score": 0.0-1.0, "weakest": "<dimension>", "criticism": "<one line>"} |
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|
