Beam AI Evaluation Framework
This framework evaluates AI agents using a structured approach to measure accuracy and performance.
Testing Dataset Setup: Define sample inputs, evaluation criteria, and expected outputs for each step.
Running the Evaluation: Execute the agent with the testing dataset to gather output for each step.
Automated Accuracy Evaluation: Compare the agent’s outputs against expected results to calculate an accuracy score (0-100%).
Monitor and Improve: Review the accuracy score of the agent to monitor and improve your agent through increasing the accuracy score.
Evaluation Setup Process
This guide walks you through the key steps in setting up an evaluation for your AI agent using Beam AI’s framework.
Create Testing Dataset
Start by creating a dedicated testing dataset. This will house the various inputs and expected outputs needed to evaluate your agent’s performance.
Define Sample Inputs
Populate the dataset with sample inputs that represent real-world scenarios your agent is likely to encounter. These inputs form the basis for testing how well the agent handles different situations.
Run Agent & Capture Outputs
Run the agent with the sample inputs you’ve defined. The agent’s responses will be recorded and can be used as a preliminary set of outputs to guide the setup of expected results.
Define Evaluation Criteria
Establish specific criteria for evaluating the agent’s responses. Criteria should focus on accuracy, relevance, and alignment with the expected outcome for each step in the workflow.
Define Expected Outputs
Use the agent’s initial responses or manually crafted ideal responses as the “golden” set of expected outputs. These expected outputs will be the benchmark for assessing agent performance in later tests.
Test & Improve Agent Accuracy
Run the full evaluation to test the agent’s responses against the expected outputs. Review the results and refine the dataset, criteria, and expected outputs iteratively to enhance agent accuracy and reliability.