5 Things Developers Get Wrong About AI Evals

1. 1Evaluate your LLM using real-world data to test its performance in practical scenarios
2. 2Test your application's functionality beyond just passing benchmarks
3. 3Consider edge cases and potential biases in your LLM's decision-making process
4. 4Validate your application's performance using multiple metrics and evaluation methods
5. 5Iterate and refine your LLM and application based on the results of your evaluations

Developers and data scientists working with LLMs can benefit from understanding the limitations of AI evaluations to ensure their applications are properly validated

💡 Passing a benchmark is not a guarantee of real-world performance

🚨 Don't assume your LLM app works just because it passed a benchmark! 🚨