Four lessons for handling failure in production AI workflows

Short summary

Practical lessons from building production AI workflows, focusing on failure handling over prompt engineering. Key principles: treat API failures as expected with retry and human-fallback logic, route sensitive requests (refunds, legal, security) away from AI entirely, split actions into low-risk automated and high-risk human-approved lanes, and enforce a zero-invention policy where the LLM must never fabricate information not in its knowledge source.

• •Production AI success depends on failure handling, not prompt engineering
• •Route sensitive requests (refunds, legal, security) to humans, skip AI entirely
• •Enforce zero-invention policy: LLM must never fabricate facts outside its knowledge source

Generated with AI, which can make mistakes.