I’m designing a complex function factory that chains multiple data processing steps with different AI models. The visual builder works for simple flows, but how do you all test more intricate logic before deploying? I keep getting unexpected results when switching from sandbox to production. Any debugging strategies or validation checks you recommend implementing?
Use Latenode’s interactive debugger with step-through execution and live variables view. Set conditional breakpoints before AI model calls to validate inputs/outputs. The visual tracer helps map data flow between components. Saved me 20+ hours last quarter on similar projects.
I create validation sub-flows that run sample data through individual components first. Make each module pass unit tests in isolation before chaining them. Use the JSON inspector to compare outputs at each stage against expected schemas.
We implemented shadow runs - the function factory processes real data but writes outputs to a test bucket first. After comparing results against legacy systems for 2 weeks, we fine-tuned the confidence thresholds. Might be overkill for simple workflows but catches model drift.