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One thing I didn’t expect when building AI automations:
The hardest part isn’t getting the LLM to work. It’s handling everything around it.
A workflow can fail even when the model gives a good answer.
What if:
The LLM returns incomplete data?
An API times out?
The extracted information doesn't match the expected format?
That’s why I’ve started thinking about AI automation as a system, not just an LLM call.
The model is only one component.
Validation, retries, fallbacks, logging, and deterministic logic matter just as much.
A working AI demo isn't necessarily a reliable AI system.
What’s been the biggest reliability challenge in the AI workflows you’ve built?
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Good structure. The part I would add is a review queue for low-confidence parsing and duplicate records. Ranking is only useful if the recruiter can see why someone matched and correct the data before it becomes the source of truth.
