Prompt Performance Index Calculator

Formula used

How to use this calculator

1. Score each prompt run using your rubric for clarity, relevance, and accuracy.
2. Enter operational metrics from logs: latency, tokens, and cost per 1k.
3. Provide success, hallucination, and satisfaction rates from evaluations.
4. Adjust quality weights to reflect what matters most in your use case.
5. Click calculate, then export CSV or PDF for reporting.

Why a Single Index Matters in Prompt Operations

Teams often compare prompts by anecdotes, then regress silently after a model upgrade. A unified Prompt Performance Index converts scattered observations into a 0–100 score that can be trended weekly. In this calculator, Quality contributes 50%, Efficiency 25%, Reliability 15%, and Satisfaction 10%. Those constants make trade‑offs explicit, so stakeholders can approve changes with the same yardstick across agents, tools, and product areas.

Building a Consistent Scoring Rubric

Quality uses six rubric signals—clarity, relevance, factuality, completeness, adherence, and safety—each scored 0–10. The weighted average prevents one weak dimension from being hidden by strong ones. When you change weights, document the reason and keep them stable for a full evaluation cycle. For example, customer support may weight adherence and safety higher, while research summarization may emphasize factuality and completeness.

Balancing Quality With Cost and Latency

Efficiency is calculated as 100 ÷ (1 + α·latency + β·cost/call + τ·tokens). This shape rewards improvements early and avoids extreme scores when metrics are near zero. Use α to reflect user patience, β to reflect budget pressure, and τ to curb verbose completions. If you halve tokens while holding quality, the index should rise enough to justify prompt compression work.

Reliability Tracking With Success and Hallucination Rates

Reliability starts from success rate across tests, then subtracts γ times hallucination rate. Success can be pass/fail against an evaluation set, while hallucination captures ungrounded claims that would fail a human audit. Separating these two inputs helps you detect “fast but wrong” prompt patterns. If reliability drops after a new template, roll back quickly and investigate which instructions introduced the drift.

Using Exports for Governance and Iteration

The CSV export supports dashboards, while the PDF report is useful for change requests and incident retrospectives. Store each run with version labels, dataset IDs, and model settings in your own tracking system. Over time, target bands such as 80+ for production assistants and 90+ for regulated workflows. Use the recommendation list as a checklist, but validate gains with the same test suite before shipping. Pair the index with brief qualitative notes to explain outliers and accelerate future root‑cause reviews across releases safely.

FAQs

What does a high PPI mean?

It indicates strong rubric quality plus acceptable cost, latency, and reliability. Use it for comparisons between prompt versions, not as an absolute guarantee of correctness in every scenario.

How should I score the 0–10 quality fields?

Use a written rubric and score from real outputs. Anchor 10 as “meets all requirements with no fixes” and 0 as “unusable.” Average multiple raters when possible.

Which weights should I change first?

Start by increasing weights for the failure mode that hurts users most. For regulated flows, raise safety and factuality. For strict formatting tasks, raise adherence and completeness.

How do I estimate cost per 1k tokens?

Use your provider pricing and a blended rate if you mix models. If you track input and output separately, compute an average per 1k total tokens for the workflow.

Why is there a safety penalty below 6/10?

Low safety signals higher risk, even if other scores look good. The penalty reduces the index by up to 30 points, pushing teams to fix guardrails before optimizing speed or cost.

How often should I recalculate PPI?

Recalculate after any prompt change, model update, or dataset refresh. For stable systems, run weekly with the same evaluation set to catch gradual drift and regression early.

Example data table