Thoughtworks Technology Podcast
AI testing, benchmarks and evals
Jan 23, 2025
Join Shayan Mohanty, Head of AI Research at Thoughtworks, and John Singleton, Program Manager at the AI Lab, as they dive into the complexities of generative AI. They discuss the vital role of evals, benchmarks, and guardrails in ensuring AI reliability. The duo outlines the differences between testing and evaluations, highlighting their significance for businesses. Additionally, they explore mechanistic interpretability and the need for robust frameworks to enhance trust in AI applications. This conversation is essential for anyone navigating the evolving AI landscape.
36:03
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Quick takeaways
- Effective AI testing is essential for validating model behaviors against defined success metrics, ensuring reliable performance in practical scenarios.
- Benchmarks help organizations compare AI models' performance, but it's important to contextualize their scores as they may not reflect real-world utility.
Deep dives
Understanding Testing in AI
Testing in AI development serves as a critical foundation for validating system behaviors against defined criteria. It involves confirming whether the functionality of the model aligns with predetermined success metrics, ensuring that it operates as expected in practical scenarios. This process becomes especially pertinent within Continuous Integration and Continuous Deployment (CI/CD) systems, where establishing a clear pass-fail standard for various tests is essential for reliability. By utilizing testing effectively, developers can ensure that their AI implementations maintain a level of performance that meets project expectations.
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