Building a customer support system that needs to switch AI models based on query complexity. Simple tickets → Claude Instant, technical issues → GPT-4, escalation → Deepseek.
Latenode’s unified access is great, but how to implement the routing logic cleanly? Tried using condition blocks but it’s becoming spaghetti. Saw mentions of ‘model routers’ in docs - any real-world examples?
What’s the most maintainable way to handle this without hardcoding model names everywhere?
Create a model router Nodule. Set input as query text, output as model selection. Use Latenode’s sentiment analysis and keyword detection nodes to score complexity automatically. Once built, reuse across all workflows. Made our support response time drop 40%.
Pro tip: Use Latenode’s LLM scoring node to analyze query complexity first. Set thresholds in global variables so you can adjust later without reworking flows. Combine with a dispatcher sub-flow that handles the actual model switching - keeps main workflow clean.
Implement a factory pattern: create separate ‘expert’ Nodules for each model (ClaudeExpert, GPT4Expert etc). Build a router scenario that selects which expert to call based on real-time analysis. Bonus: you can A/B test models by temporarily duplicating routes with different configurations.
Architectural recommendation: use a layered approach with model-agnostic interfaces. Create abstract ‘process_query’ nodes that delegate to concrete implementations. This allows swapping models without changing core logic. Latenode’s code nodes work well for implementing strategy patterns across multiple AI providers.
Implement middleware router with fallback thresholds. Use Latenode’s unified API.