Quantify response quality with weighted partial credit. Track accuracy, error rate, and confidence bounds. Export clear reports for fast iteration.
| Prompt batch | Total | Correct | Partial | Incorrect | Weight | Accuracy (%) |
|---|---|---|---|---|---|---|
| Safety prompts | 50 | 41 | 6 | 3 | 0.50 | 88.00 |
| Math prompts | 40 | 25 | 10 | 5 | 0.50 | 75.00 |
| Tool-use prompts | 60 | 39 | 15 | 6 | 0.70 | 82.50 |
Example figures are illustrative. Use your own rubric and test set.
Reliable accuracy reporting starts with a fixed evaluation set and a consistent rubric. Separate prompt families by intent, such as safety, reasoning, retrieval, and tool use, because each group carries different failure modes. Track the proportion of correct, partially correct, and incorrect outputs, then apply a documented partial credit weight so teams interpret “good enough” the same way across releases.
Binary scoring can hide meaningful improvements when responses are mostly helpful but incomplete. Weighted accuracy treats partial answers as fractional wins, improving sensitivity without inflating performance. For example, giving 0.50 weight to partial outputs increases signal when outputs contain correct structure yet miss a constraint, citation, or unit conversion. This calculator converts those counts into a single comparable percentage.
A single accuracy value can mislead when sample sizes are small. The built in 95% interval uses a Wilson score approach to estimate the plausible range of true accuracy. As total prompts rise, the interval narrows, letting you distinguish meaningful change from noise. Use the interval when comparing models, prompt templates, or retrieval configurations across experiments.
Many production systems gate answers using a confidence score, refusal policy, or verification step. Raising the minimum threshold typically reduces risky outputs but may lower coverage. Recording an acceptance rate alongside accuracy clarifies the tradeoff between quality and throughput. When accuracy is high but acceptance is low, users may experience delays, extra turns, or fewer completed tasks.
Operational accuracy improves fastest when metrics are reviewed on a steady cadence with drill downs. Export CSV for dashboards and PDF for reviews, then segment by prompt category, language, and difficulty. Monitor drift by re running the same set after model updates and by adding fresh prompts that reflect current user behavior. Pair the metric with error taxonomy notes to guide prompt, policy, and data fixes. Include inter-rater checks to keep labeling stable, and store representative examples for each class. When partial credit changes, backfill prior runs for continuity. Over time, the metric becomes a compact contract between product goals and model behavior clearly.
Use partial when the answer is useful but misses at least one required element, such as a constraint, a step, a unit, or a key fact. Document examples so labelers apply the rule consistently.
Pick a weight that matches real user value. If partial answers usually need a small fix, use 0.7–0.9. If they are only a starting point, use 0.3–0.6. Keep the weight stable during comparisons.
It shows uncertainty from limited samples. Two models with close accuracy can be statistically indistinguishable when the interval is wide. Larger prompt sets narrow the interval and improve decision confidence.
You can, but also report per category. Aggregates hide where failures concentrate, such as safety or tool usage. Category breakdowns guide targeted improvements and prevent regressions in high risk areas.
It approximates how many outputs pass a confidence gate. In production, acceptance is set by policies, verification, or thresholds. Track it alongside accuracy to understand the quality versus coverage tradeoff.
Rerun after model, retrieval, or policy changes, and on a regular cadence like weekly. Keep a stable core set for trend lines, and add a small rotating set to capture new user patterns.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.