How does deterministic variant assignment work in A/B testing?

It uses a hash of the user ID and experiment ID to ensure a specific user consistently receives the same prompt variant, preventing a disjointed user experience during experiments.

Can this skill help reduce LLM operational costs?

Yes, by tracking cost-per-task and tokens-per-variant, you can identify which prompt structures achieve high-quality results using fewer tokens or less expensive model calls.

Why is prompt versioning necessary for AI agents?

Prompt versioning ensures reproducibility, allows for immediate rollbacks of underperforming updates, and enables developers to link specific agent behaviors to the exact instructions used.

Does this skill require specific observability platforms?

While the examples utilize Langfuse, the core principles of attribute tagging and version tracking can be applied to any observability stack or custom logging solution.

How do I detect if a new prompt version is a regression?

The skill provides patterns to compare the scores of a new version against a baseline across key metrics like task completion and latency, identifying statistically significant performance drops.

Prompt Versioning & A/B Testing

Name: Prompt Versioning & A/B Testing
Author: nexus-labs-automation

bynexus-labs-automation

分析と監視

Tracks LLM prompt versions and conducts A/B experiments to optimize agent performance, quality, and cost.

概要

This skill provides a structured framework for managing LLM prompt iterations, enabling developers to version-control templates, run deterministic A/B tests, and monitor production performance across different variants. By implementing standardized metadata capture—including template hashes, variables, and version IDs—it facilitates data-driven prompt engineering. Users can leverage built-in patterns for regression detection, performance analysis (latency, cost, and success rates), and safe gradual rollouts, ensuring that prompt updates consistently improve agent behavior rather than introducing unforeseen issues.

主な機能

Comprehensive metadata tracking for prompt templates and hashes
Automated regression detection against baseline performance
Gradual rollout strategies for safe production deployments
0 GitHub stars
Deterministic A/B testing with weighted variant assignment
Performance monitoring across quality, cost, and latency metrics

ユースケース

Measuring the cost-benefit ratio of switching between complex and concise prompts
Comparing system instructions to improve agent task completion rates
Safely deploying prompt updates to a small subset of users before a full release

概要

主な機能

Comprehensive metadata tracking for prompt templates and hashes
Automated regression detection against baseline performance
Gradual rollout strategies for safe production deployments
0 GitHub stars
Deterministic A/B testing with weighted variant assignment
Performance monitoring across quality, cost, and latency metrics

ユースケース

Measuring the cost-benefit ratio of switching between complex and concise prompts
Comparing system instructions to improve agent task completion rates
Safely deploying prompt updates to a small subset of users before a full release