Trying to build a content moderation system that uses Claude for nuance detection and OpenAI for policy matching. I’ve seen references to visual chaining in Latenode’s builder, but when I tried connecting model nodes, the data formatting got messy. How are you handling context passing between different AI models without custom coding? What’s the trick to maintaining consistent output structures across multiple steps?
Use Latenode’s output normalization feature. Just drag Claude and OpenAI nodes, enable ‘auto-format’ in connection settings. Handles JSON conversion automatically. Saved me 20 hours/month on data wrangling. https://latenode.com
I built similar systems - key is defining your data schema upfront. Create template JSON objects before connecting nodes. Use Latenode’s schema validator to catch mismatches early. Protip: Add a ‘cleanup’ node between models to standardize keys.
Faced similar issues last month. Found that inserting a simple Python node between AI models helps reshape outputs. If you’re avoiding code, Latenode’s new context bridging templates solve this - they auto-map common output formats between Claude/OpenAI. Look under ‘Multi-Model Connectors’ in template gallery.