What this prompt does
This prompt builds a prompt management and A/B testing system for a [app_type] running [volume] LLM calls per day. It covers prompt version control with diff and rollback in [storage_backend], A/B configuration splitting traffic [split_ratio], collection of [metrics], a statistical-significance calculator requiring at least [min_samples] samples, gradual rollout (5% to 25% to 50% to 100%) with automatic rollback on quality drop, a template engine with variable injection, environment separation, an audit trail, cost comparison by tokens and pricing, and a dashboard — implemented in [language] with a REST API.
The structure works because once an LLM feature is live, prompts behave like deployable code: a small wording change can shift quality, latency, and cost. Treating prompts with versioning, A/B tests, and gradual rollout with automatic rollback is what lets you iterate safely on real traffic. The [min_samples] significance gate is the discipline that stops you from declaring a winner on noise.
When to use it
- When an LLM feature is live and prompt changes affect real users
- When you want to A/B test prompt variants at
[split_ratio]on production traffic - When you need version history, diffs, and one-click rollback for prompts
- When measuring
[metrics]like quality, latency, and cost across variants - When rolling out a new prompt gradually with automatic rollback on regressions
- When comparing token cost between variants before committing to one
Example output
You get a [language] implementation with a REST API: prompt storage and versioning with a diff viewer and rollback in [storage_backend], A/B test configuration splitting traffic at [split_ratio], metrics collection for [metrics], the significance calculator gated on [min_samples], gradual-rollout logic stepping 5% to 25% to 50% to 100% with automatic rollback on a quality drop, a template engine with variable injection and conditional sections, environment management separating dev, staging, and production prompts, an audit trail of who changed what, cost comparison by tokens times pricing, and a dashboard surfacing active experiments and historical results. It is a production-oriented system built for [volume] daily calls rather than a one-off script.
Pro tips
- Treat prompts as deployable code — the versioning and rollback in
[storage_backend]are what make iteration safe rather than risky - Do not declare an A/B winner before
[min_samples]is reached; small samples produce confident-looking but meaningless results - Include cost in
[metrics]from day one, since a slightly better prompt that doubles token usage may not be worth shipping - Lean on the gradual rollout with automatic rollback so a quality regression is caught at 5% rather than across all
[volume]calls - Keep an LLM-judge quality score honest by spot-checking it against human review; automated scores can drift
- Separate dev, staging, and production prompts so an experiment never leaks an untested variant to all users
