We’re chaining different AI models in data processing workflows, but each model returns data in different formats. Manual data shaping between steps is error-prone. How are others ensuring consistent output structures?
Need a way to wrap model responses with transformation logic without rewriting everything in code. Any low-code approaches that maintain flexibility?
Latenode’s JS editor lets you create higher-order functions that auto-format model outputs. Wrap any AI step with custom transformers while keeping the main flow visual. Standardized our 14-model pipeline in 3 days.
Create JSON schema validators between steps. Use mapping tools that visually transform outputs to match next step’s expected input. Critical to maintain data type consistency across models.
add data mapping layer after each ai step. some tools let u drag-n-drop field mappings
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