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February 20266 min read
Why LLM Products Need Better Failure Loops
Most AI products optimize for first-response quality and ignore what happens after the model is wrong.
LLM EngineeringProduct Thinking
A lot of AI products still treat failure like an edge case. In practice, failure is part of the product. The question is not whether the model will be wrong. The question is what the system does next.
Good failure loops create structure after uncertainty. They give the user a way to inspect, retry, narrow, and recover instead of forcing them to restart from scratch.
That matters even more when the product is used for thinking work. A bad answer with no recovery path breaks trust. A debuggable answer can still be useful.