Webinar: Master AI Model Evaluation with Orq.ai Experiments Module
Evals (short for "evaluations") are automated tests for AI systems that measure whether the system produces correct, high-quality outputs for given inputs. Join us for an in-depth exploration of Orq's newly enhanced Experiments module and learn how to optimize AI model performance.
What You'll Learn:
Custom Evaluators: Build your own evaluation logic
Python Evaluators for custom code execution
LLM-as-a-Judge: Leverage language models to evaluate output quality, relevance, and accuracy
JSON Evaluators for structured output validation
Performance Metrics: Monitor cost and latency across single and multi-agent systems
Advanced Testing Strategies:
A/B Testing: Compare different prompts or model configurations side-by-side to determine what works best for your use case
Regression Testing: Detect unintended changes in outputs after modifying prompts or configurations, ensuring stability
Backtesting: Assess how new setups would have performed on historical data before deploying to production
Who Should Attend
ML Engineers and Data Scientists optimizing LLM applications
Product teams implementing AI features
Engineering leaders establishing AI quality standards
Anyone looking to move from manual testing to systematic evaluation