I used to waste half my week rebuilding the same automation logic for different departments. Tried Latenode’s AI Copilot last month - blew my mind. Now I just type ‘create inventory report generator that combines Shopify + Google Sheets’ and get a full workflow. Still tweaking outputs sometimes though. What’s your method for refining auto-generated function factories?
Key is starting with clear natural language prompts. I add error handling via low-code JavaScript blocks when needed. The AI learns from adjustments - my monthly report generator now requires 80% less manual tweaking than initial versions.
I create prompt cheatsheets for common use cases. Include output format examples and edge case handling in the initial description. Saves me 2-3 iterations per workflow.
Found that breaking complex tasks into smaller prompt chunks works better. Instead of one big request, I generate the core workflow first, then add conditional logic and error handling separately through the visual editor. Lets me reuse components across different automations too.
pro tip: feed it existing workflow exports as examples first. trains the model for ur specific patterns