Curious what this community's run into — inconsistent output formatting breaking a downstream step, rate limits on batch runs, a model just not following instructions reliably. What's actually been the hard part, in your experience, versus what you expected going in?
+11Hi
In the past I’ve had AI actions not following my instructions consistently, so it’s taken a bit of trial and error with adjusting the prompt or model to get them working correctly. I'd guess that's a pretty common one. Keen to know what experiences others have had!
The biggest thing for me: treat the AI step as a probabilistic action, not a deterministic one — then design the Zap around that.
Three patterns that fixed most of my headaches:
1. Demand structured output and validate it. Ask the model for JSON (or use a schema-based AI step) and put a validation step right after — Formatter or Code to check required keys exist. If it fails, route to a retry instead of letting malformed output poison downstream steps.
2. Never let AI output touch a critical action directly. AI writes the email draft → a Paths step or human-approval step runs before the Send Email action fires. AI suggests a deal value → store it in a custom field for review before the CRM update. The AI step proposes, something deterministic disposes.
3. Retry with error feedback, not blind retries. When output is bad, feed the failed attempt back into the retry prompt ("your previous output failed because X, try again") instead of sending the same prompt. Second attempts with failure context succeed far more often.
Same discipline as any flaky third-party API: validate the response shape, gate the critical side effects, and build retries that actually learn from the failure.
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