I Intentionally Built a Bad Decision System (So You Don't Have To)
The author intentionally built a bad AI system to understand how systems can fail silently when design principles are ignored, and compared it to a well-designed version of the same pipeline.
Why it matters
Understanding these silent failure modes can help developers build more robust and reliable AI/ML systems.
Key Points
- 1The author built a bad AI system and a good AI system to solve the same problem: input text -> extract keywords -> compute a score -> recommend an action
- 2The bad system exhibited multiple failure modes like drift, non-determinism, hidden state, and silent corruption
- 3These failure modes are common in real-world AI and data systems, and naming them makes them easier to detect and harder to accidentally ship
Details
The author's goal was not performance, but to understand how systems fail silently when design principles are ignored. Both systems solve the same problem: input text -> extract keywords -> compute a score -> recommend an action. The benchmark involves running the same input multiple times through each system and observing the outputs. The bad system exhibited issues like score accumulation, non-deterministic outputs, and hidden state, leading to drift, non-determinism, and silent corruption. These failure modes are common in real-world AI and data systems, and naming them makes them easier to detect and harder to accidentally ship.
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