Prove Your Prompt Works — With Statistical Evidence
Stop guessing which prompt is better. Run 10-50 trials per experiment, get t-distribution confidence intervals, and compare results across GPT-4, Claude, and Gemini — all as a team.
How Experiments Work
Four steps from raw data to statistically validated prompts
Upload & Label Data
Upload images, PDFs, or videos with ground truth labels. Your team's shared evaluation dataset ensures everyone tests against the same standard.
Create & Configure Prompts
Write extraction prompts and configure model parameters (temperature, top_p). Try different models — GPT-4o, Claude Sonnet, Gemini Pro — with the same prompt.
Run Statistical Experiments
Choose 10-50 trials per experiment. The batch engine runs every prompt × data item × trial combination in the background. Choose 90%, 95%, or 99% confidence level.
Analyze & Compare Results
View confidence intervals, field-level accuracy breakdown, model comparisons, and latency percentiles. Identify exactly which fields need improvement.
Compare Models Side by Side
Run the same prompt across multiple LLM providers in a single experiment. Find the best model for your specific extraction task.
OpenAI
GPT-4o, GPT-4o Mini
Anthropic
Claude Sonnet, Claude Opus, Claude Haiku
Gemini 2.5 Pro, Gemini 2.5 Flash
Same prompt, same data, different models — statistically compared.
Every Detail, Statistically Backed
Go beyond simple accuracy scores. Understand exactly where your prompt succeeds and where it fails.
Confidence Intervals
T-distribution analysis gives you 95% confidence bounds. Know if prompt A is truly better than prompt B, not just lucky.
Field-Level Breakdown
See accuracy per extraction field. If 'invoice_number' is at 98% but 'total_amount' is at 72%, you know exactly what to fix.
Latency Tracking
P50, P75, P95 latency percentiles per model. Balance accuracy against speed for production use.
Failure Analysis
Breakdown of auth errors, rate limits, and API failures. Debug issues before they reach production.
The Right Role for Every Team Member
Not everyone on your team writes prompts — but everyone can contribute to prompt quality.
Admin
Manage team members, set roles, handle billing and organization settings.
Experimenter
Upload data, create prompts, run experiments, and analyze results. The core prompt engineer role.
Prompt Improver
Focus on writing and refining prompts without needing to manage data or experiments.
Data Quality Assurer
Ensure ground truth labels are correct and consistent. The foundation of reliable experiments.
Ready to Improve Your LLM Prompts?
Start testing with statistical rigor today
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