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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AI Product Development's New Reality
- AI product development demands continuous iteration post-release, unlike traditional software.
- Real-world "torture tests" reveal edge cases only seen in production, essential for true evaluation.
Test AI Features Thoroughly
- Use rigorous A/B testing to ensure AI features truly improve product performance.
- Monitor key metrics closely to avoid damaging core AI functionality.
Align Evaluations with Real Use
- Industry benchmarks often don't reflect real user environments, leading to misleading evaluation results.
- Custom evaluations must match actual user contexts for meaningful AI improvement.