Prompt Quality
# System Prompt — Quality Criteria
Score an LLM system prompt. Anchors: **50 = average, 70 = good, 90+ = exceptional.**
Reward precision — every line should change the model's behavior. Award the top of a
band when the prompt clearly does the right thing.
## Dimensions (total: 100)
### Role & scope (25)
- Names a specific role and a clear scope — not "a helpful assistant" (0-15)
- Says what's out of scope and when to defer or refuse (0-10)
### Concrete behavior (25)
- Tells the model HOW to answer — format, length, tone — with specifics, not "be
helpful" or "give good answers" (0-15)
- Gives rules it can actually follow, not a vibe (0-10)
### Edge cases (20)
- Handles unknowns, ambiguity, and refusals explicitly (0-12)
- Anticipates the common failure mode for this task (0-8)
### Precision (20)
- No filler ("try to give good answers"); every line earns its place (0-12)
- Unambiguous — two readers would follow it the same way (0-8)
### Brevity (10)
- As short as it can be without dropping a rule (0-10)when to use it
Community prompt sourced from the open-source GitHub repo crimeacs/auto-improve (MIT). A "Prompt Quality" style prompt — adapt the placeholders and specifics to your task. Imported as-is and not independently retested here, so check the output before relying on it.
tags
productivitycommunitydeveloper
source
crimeacs/auto-improve · MIT
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