Compare prompt variants across quality, conversions, and token spend. Quantify uplift with confidence-based significance checks. Choose winning prompts using cleaner evidence, not intuition alone.
| Variant | Impressions | Successful Outputs | Avg Quality Score | Avg Tokens | Token Cost / 1K |
|---|---|---|---|---|---|
| A | 1000 | 120 | 3.8 | 650 | $0.50 |
| B | 1000 | 145 | 4.2 | 700 | $0.50 |
Conversion Rate: rate = successes / impressions
Absolute Uplift: uplift = rateB − rateA
Relative Uplift: (rateB − rateA) / rateA
Pooled Rate: p = (xA + xB) / (nA + nB)
Z Score (two-proportion): z = (rateB − rateA) / √(p(1−p)(1/nA + 1/nB))
P-Value: Two-tailed p-value derived from the standard normal distribution.
Confidence Interval (uplift): uplift ± zcritical × SEdifference
Cost per Success: ((avg tokens / 1000) × token cost) / successes
Sample Estimate: Approximate balanced sample size uses baseline rate, target MDE, 95% confidence, and 80% power.
Start by defining one primary success event for both prompts, such as accepted output, click-through, or completed task. Keep traffic sources, audience segments, and model settings stable. Record impressions, responses, and exclusions consistently. This calculator works best when each variant is exposed to similar conditions, because balanced sampling reduces bias and improves interpretation of uplift, significance, and confidence intervals across testing cycles.
Prompt experiments often improve conversion while reducing response quality, or improve quality while increasing token use. The calculator combines success counts with optional average quality scores so teams can review trade-offs instead of chasing a single metric. In production workflows, a conversion lift may be rejected if quality drops below service thresholds or compliance expectations defined by the team.
The z-test section estimates whether observed differences are likely due to real performance changes or random variation. P-values support decision discipline, while confidence intervals show the plausible uplift range. For example, a positive uplift with a wide interval crossing zero signals uncertainty, not a reliable win. Use the recommended sample estimate to plan stronger follow-up tests before rolling changes into customer-facing systems.
Token consumption directly affects operating cost, especially for high-volume prompts in support, lead qualification, and document processing. By adding average tokens and token pricing, the calculator estimates cost per successful outcome for each variant. This helps teams compare financial efficiency, not only response rate. A prompt with slightly lower conversion can still be the better choice if token savings materially improve unit economics.
Run tests long enough to cover demand fluctuations, then archive results in CSV and PDF for audits, sprint reviews, and prompt libraries. Re-test winners after major model updates, policy changes, or routing adjustments. Document prompt text, guardrails, and evaluation criteria with each experiment. Consistent process discipline turns this calculator into a repeatable decision framework for prompt optimization programs.
A successful outcome is your chosen pass condition, such as accepted response, resolved ticket, click, or completed workflow. Use one consistent definition for both variants.
Yes, balanced traffic is preferred. Equal exposure simplifies interpretation, improves statistical stability, and reduces bias when comparing conversion rates and confidence intervals.
No. This calculator is built around A/B conversion testing. Quality score is an additional comparison signal, not a replacement for impressions and successes.
It indicates the observed performance gap is unlikely from random sampling alone at the selected threshold. It supports confidence, but context and data quality still matter.
The interval shows the likely range of true uplift. A wide range means uncertainty, while a narrow range supports stronger rollout decisions.
Retest after model upgrades, policy changes, audience shifts, or routing updates. Prompt performance can drift, so periodic validation helps maintain reliable production results.
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.