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The AI Native Dev - from Copilot today to AI Native Software Development tomorrow
AI Evaluation and Testing: How to Know When Your Product Works (or Doesn’t)
Dec 10, 2024
Des Traynor, founder of Intercom, shares insights on how generative AI is reshaping product development. Rishabh Mehrotra from Sourcegraph emphasizes the need for robust evaluation processes over mere model training. Tamar Yehoshua, President of Glean, discusses the challenges of using large language models in sensitive data environments. Simon Last, co-founder of Notion, highlights the importance of continuous improvement and iterative development. Together, they provide a captivating look at ensuring AI products are effective and reliable.
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Quick takeaways
- Generative AI challenges traditional product development by requiring developers to prioritize technical capabilities before identifying user problems for effective solutions.
- The focus on creating realistic evaluation datasets is crucial, as it helps ensure that measurements effectively reflect real-world user interactions and improve user experience.
Deep dives
The Importance of Real-World Testing
Evaluating AI products requires testing in real-world scenarios, as highlighted by Dez Trainor's concept of torture tests. These tests examine how well a product performs under stressful and unpredictable conditions that users may encounter. The effectiveness of changes made to models or prompts can only be confirmed once the product is deployed using actual production data. This understanding challenges the belief that results can be gauged in a binary fashion, underscoring the need for a spectrum-based approach to success metrics.
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