Introduction

Experiments in HoneyHive help you systematically test and improve your AI applications. Whether you’re iterating on prompts, comparing models, or optimizing your RAG pipeline, experiments provide a structured way to measure improvements and ensure reliability.

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What is an experiment?

An experiment in HoneyHive consists of three core components:

1. Application Logic: The components you want to evaluate - this could be different models, prompts, retrieval strategies, or any configuration you want to test.
2. Dataset: The inputs you’re evaluating against. Using consistent test data ensures you can reliably compare different versions of your application.
3. Evaluators: The metrics and criteria you’re measuring. Evaluators help quantify improvements and catch regressions across different configurations.

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Why run experiments with HoneyHive?

Experiments provide a systematic approach to improving your AI applications:

• Iterate with confidence: Test prompt variations, model configurations, and architectural changes against consistent metrics
• Track improvements: Monitor how changes affect key metrics over time and ensure continuous improvement
• Automate quality checks: With GitHub integration, automatically run experiments on code changes and set performance thresholds
• Compare approaches: Evaluate different models, retrieval methods, or chunking strategies using standardized metrics
• Ensure reliability: Catch potential issues by testing across diverse scenarios before deploying to production

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How do experiments work?

HoneyHive uses metadata linking to track and organize experiment traces:

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Trace Metadata and Linking

Every trace in HoneyHive contains metadata that links it to specific experiments and inputs. The run_id in the metadata links related test traces together, while the datapoint_id connects traces that were run on the same input.

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Experiment Structure

1. Experiment-Dataset Relationship

   • Each experiment run (identified by run_id) is linked to a specific dataset
   • This dataset-run linking enables aggregate comparison across different configurations
   • Multiple runs can use the same dataset, allowing you to test different approaches against consistent inputs

2. Trace Comparison

   • Traces with the same datapoint_id represent different configurations tested on identical inputs
   • This enables direct comparison of performance for specific inputs
   • Example: Compare how different LLM models handle the same prompt, or how different RAG configurations retrieve for the same query

3. Performance Tracking

   • Evaluators measure performance metrics for each trace
   • Results can be analyzed at both individual trace and aggregate run levels
   • Metrics are tracked over time to identify improvements or regressions

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Integration with Development Workflow

The experiment framework integrates with GitHub to:

• Trigger automated experiment runs on code changes
• Set performance thresholds that must be met
• Track metric improvements across commits
• Alert on performance regressions

This metadata-driven approach to testing and evaluation lets you compare performance across any configuration dimension - whether you’re testing different prompts, models, or entire pipeline architectures.