Evals

1. 1Apply consistency evals to measure model stability across different inputs
2. 2Use avoidance evals to identify potential biases in model responses
3. 3Run limitation evals to detect areas where the model lacks knowledge or understanding
4. 4Compare eval results to refine model performance and address weaknesses
5. 5Configure model training data to address identified limitations and biases

Data scientists and AI engineers can benefit from these evals to improve model reliability and robustness

💡 Evaluating AI models beyond accuracy is crucial for reliable and robust performance

🤖 Evaluate AI models beyond accuracy with 3 behavioral evals: consistency, avoidance, and limitations #AI #MachineLearning