During AI agent development, you’ll frequently iterate on your agent to achieve optimal performance. Whether you’re tweaking model parameters, testing different model versions, or refining prompts, each change affects your agent’s output quality.However, with multiple iterations, it becomes challenging to accurately quantify and compare the effectiveness of your changes. Scorecard’s A/B Comparison feature solves this by providing side-by-side run comparisons, giving you clear, data-driven insights into your improvements.
Requirements: Only runs using the same Testset can be compared with each other.
Include multiple metrics that cover different aspects of your system (accuracy, relevance, safety, etc.) to get a complete picture of performance changes.
Test with Sufficient Data
Ensure your testset has enough examples to make statistically significant comparisons. Small testsets may lead to misleading conclusions.
Document Your Changes
Keep track of what specific changes you made between runs so you can understand which modifications led to improvements.
Compare different approaches to your LLM pipeline, such as RAG implementations, context window usage, or post-processing steps.
Remember that A/B comparisons are only as good as your metrics and testset. Ensure your evaluation criteria accurately reflect real-world performance requirements.
