Eval-Driven Agentic AI Development: The Most Important Practice Nobody Is Doing (And What I Got…
📰 Medium · AI
Learn to build automated CI/CD evaluation gates for AI systems using real production failures, not assumptions, to improve AI development
Action Steps
- Build automated CI/CD evaluation gates for AI systems using real production failures
- Configure evaluation gates to test AI systems against real-world scenarios
- Test AI systems using real production data to identify potential failures
- Apply eval-driven development to improve AI system reliability
- Compare evaluation results to inform product decisions and prioritize improvements
Who Needs to Know This
AI engineers and developers can benefit from this practice to ensure reliable AI systems, while product managers can use it to inform product decisions
Key Insight
💡 Using real production failures to build automated CI/CD evaluation gates can significantly improve AI system reliability
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🚀 Improve AI development with eval-driven agentic AI development! 🤖
Key Takeaways
Learn to build automated CI/CD evaluation gates for AI systems using real production failures, not assumptions, to improve AI development
Full Article
Moving from 20 to 200 cases. How to build automated CI/CD evaluation gates for AI systems using real production failures, not assumptions. Continue reading on Predict »
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