AI Evals: The Missing Piece for Confident AI App Deployment

Stop Shipping AI Apps Scared

TLDR: AI evaluations (evals) are essential for production AI apps. Without them, adding new features breaks existing functionality, creating fear around every code change. This is especially critical for non-deterministic agents where traditional testing falls short.

The problem described here resonates with anyone who has shipped an AI application: you build a proof of concept, push it to production, and start iterating. Then you notice that changes in one area mysteriously break features elsewhere. The non-deterministic nature of AI systems makes this particularly insidious - the same input might produce different outputs, making traditional regression testing inadequate.

AI evals fill this gap by providing systematic ways to measure whether your AI system still performs correctly across its intended use cases. Unlike unit tests that check for exact outputs, evals assess whether the AI behavior meets quality thresholds - is the response helpful? Does it follow the expected format? Does it avoid hallucinating? These are judgment calls that require different evaluation approaches.

The challenge is that most development teams treat evals as an afterthought rather than a core development practice. They build the feature, maybe test it manually a few times, and ship. Then wonder why production behavior degrades over time as they add complexity.

For architects and teams building AI applications, the key insight is to treat evals as first-class citizens in your development workflow, not optional extras. This means establishing baseline evals before shipping your MVP, creating new evals whenever you add capabilities, running evals as part of CI/CD pipelines, and setting quality gates that prevent regressions from reaching production.

The specific implementation matters less than the discipline. Whether you use human evaluation, LLM-as-judge patterns, or deterministic checks depends on your use case. What matters is having systematic measurement that gives you confidence to iterate without fear.

Key takeaways:

• AI evals are essential for production AI apps, especially non-deterministic agents
• Without evals, new features frequently break existing functionality
• Traditional testing approaches don't adequately cover AI behavior quality
• Treat evals as a core development practice, not an afterthought
• Running evals in CI/CD prevents regressions from reaching production

Tradeoffs:

• Comprehensive evals increase deployment confidence but add development overhead and slow iteration cycles
• Automated LLM-as-judge evals scale better but sacrifice the nuance of human evaluation

Link: Stop shipping AI apps scared

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