Evaluation Dimensions
Accuracy
The accuracy of the Agent’s response using LLM-as-a-judge methodology.
Agent as Judge
Evaluate custom quality criteria using LLM-as-a-judge with scoring.
Performance
The performance of the Agent’s response, including latency and memory footprint.
Reliability
The reliability of the Agent’s response, including tool calls and error handling.
Quick Start
Here’s a simple example of running an accuracy evaluation:quick_eval.py
Best Practices
- Start Simple: Begin with basic accuracy tests before progressing to complex performance and reliability evaluations
- Use Multiple Test Cases: Don’t rely on a single test case—build comprehensive test suites that cover edge cases
- Track Over Time: Monitor your eval metrics continuously as you iterate on your agents
- Combine Dimensions: Evaluate across all three dimensions for a holistic view of agent quality
Next Steps
Dive deeper into each evaluation dimension:- Accuracy Evals - Learn LLM-as-a-judge techniques and multiple test case strategies
- Agent as Judge Evals - Define custom quality criteria with flexible scoring strategies
- Performance Evals - Measure latency, memory usage, and compare different configurations
- Reliability Evals - Test tool calls, error handling, and rate limiting behavior