| Prompt | Use case | Final score | Tier |
|---|---|---|---|
| Extract invoice totals from text | Document AI | 86.4 | Strong |
| Write ad copy for a new app | Marketing | 74.8 | Good |
| Diagnose server error logs | DevOps | 91.2 | Elite |
| Summarize meeting notes | Productivity | 67.9 | Fair |
1) Weighted rubric score (0–100)
Each criterion is scored from 0 to 10, converted to a percentage, then multiplied by its weight. All weights sum to 100.
2) Risk penalty
Risks are aggregated using an RMS-style average to avoid a single tiny risk dominating. Penalty strength scales the impact.
Sub-indices (Instruction, Context, Alignment, Evaluation) are simple averages mapped to 0–100. They help diagnose what to fix first.
- Paste your prompt and select the scenario fields.
- Score the rubric from 0–10 using a shared standard.
- Rate risks based on ambiguity, missing info, and safety.
- Choose weights that match your environment.
- Submit to view the tier, indices, and recommendations.
- Download CSV/PDF for tracking and change logs.
- Iterate by improving the lowest index first.
Why a benchmark score beats ad-hoc reviews
Teams often eyeball prompts, creating drift and inconsistent outputs. A 0–100 benchmark enforces repeatability and supports trend tracking across releases. In many programs, prompts above 80 need fewer revisions, while prompts below 60 trigger rework loops and unclear deliverables. Capture monthly snapshots by use case, then target a 10‑point gain per iteration by fixing the lowest sub‑index and rewriting requirements in measurable terms, for every model you support today.
Rubric criteria that predict reliability
Clarity, constraints, and structure drive the Instruction Index and predict deterministic behavior. Moving clarity from 5 to 8 can add 3–5 points under balanced weights. Context and grounding reduce missing‑information gaps, improving factual robustness. Examples and evaluation criteria help models self‑check before answering. Safety and persona anchor tone boundaries; if either is vague, reviewers typically raise risk scores and lower deployment readiness for customer, finance, and healthcare workflows, especially.
Risk penalties reveal hidden failure modes
The calculator aggregates ambiguity, missing information, hallucination risk, and toxicity risk using an RMS penalty, so one severe risk meaningfully lowers the score. For example, a risk RMS of 7 at strength 1.0 subtracts about 15 points, often dropping a Good prompt into Fair. If temperature exceeds 1.0, output variance rises; treat each 0.2 increase above 1.0 as a stability cost during production testing, in high stakes domains.
Choosing the right weight profile for your environment
Balanced weights work for cross‑team baselines and mixed use cases. Production mode prioritizes clarity and constraints, improving SOPs, support scripts, and operational consistency. Creative mode increases persona and examples, boosting style fidelity and ideation throughput. Compliance mode emphasizes safety and constraints for regulated content. When using custom weights, keep the set stable across quarters and normalize to 100 so scores remain comparable in audits and retrospectives, with clear owners and cadence.
How to operationalize benchmarking at scale
Operationalize benchmarking with a shared scoring guide that defines what 3, 6, and 9 mean for each rubric item. Run a calibration session with five prompts and compare results; aim for under 10% variance across reviewers. Store CSV exports per version and attach PDF summaries to change requests. Track median score, risk index, and the most frequent failing criterion to prioritize templates and prompt libraries, and set quarterly targets for sustained improvement.
FAQs
1) What does the final score represent?
The final score is a weighted rubric score minus risk and volatility penalties. It summarizes prompt structure quality, not model intelligence. Use it to compare prompt versions consistently.
2) How should we score rubric items consistently?
Create a short scoring guide with examples for scores 3, 6, and 9. Calibrate reviewers using a shared set of prompts until score variance stays within an acceptable band.
3) Why do risks reduce the score so strongly?
Risks indicate failure likelihood, even when the prompt looks good. The RMS aggregation ensures one severe issue, like ambiguity or hallucination risk, meaningfully affects the benchmark.
4) When should we use custom weights?
Use custom weights when your domain has unusual priorities, such as strict compliance or heavy creativity. Keep the weights stable across time to preserve comparability between runs.
5) Can I benchmark prompts for RAG systems?
Yes. Increase grounding and evaluation emphasis, and document allowed sources. Add acceptance checks like citation requirements and unknown handling to reduce hallucination risk.
6) How often should we export CSV and PDF?
Export CSV for every prompt change that impacts behavior, and attach PDF summaries to reviews or releases. This creates an auditable trail of improvements and decision rationale.