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HUMANLOOP
Evaluate and improve every LLM output with human feedback
ABOUT
Evaluating LLM outputs is subjective and slow. Teams rely on anecdotal testing — running a few prompts manually, deciding if the output "looks right," and shipping changes without systematic validation. HumanLoop gives teams a structured evaluation workflow: version-controlled prompts, test data sets, automated evaluation metrics, and human review queues. Every prompt change is benchmarked against historical test cases, and flagged outputs go to human reviewers who provide feedback that feeds back into model improvements.
INSTALL
pip install humanloopINTEGRATION GUIDE
1. Run systematic A/B tests on prompt changes with version-controlled prompt templates and historical test sets
2. Set up human review queues where flagged LLM outputs go to domain experts for quality scoring
3. Track LLM evaluation metrics like accuracy, relevance, and safety across every deployed prompt version
4. Create test data sets from real user interactions to validate prompt changes before production deployment
5. Monitor prompt regression by comparing new prompt versions against historical performance baselines
TAGS
monitoringevaluationprompt-managementllmhuman-feedbackexperimentationpythonversion-control