Prompt Routing & Conditional Logic: Building Intelligent AI Workflows
By Learnia Team
Prompt Routing and Conditional Logic: Intelligent AI Workflows
This article is written in English. Our training modules are available in multiple languages.
In a real workflow, not every input should go through the same pipeline. Customer complaints need different handling than feature requests. Technical questions need different models than creative tasks. Prompt routing adds intelligence to your chains — dynamically selecting which prompt, model, or pipeline to run based on the input.
Why Routing Matters
A single prompt optimized for customer complaints will perform poorly on technical questions, and vice versa. Routing solves this by:
- →Classifying the input first
- →Selecting the specialized prompt for that classification
- →Processing with the optimal prompt/model combination
The Three Routing Patterns
Pattern 1: Classification-Based Routing
Pattern 2: Confidence-Based Routing
Building a Complete Router
Advanced: Fallback and Error Paths
Test Your Understanding
Next Steps
You now know how to build intelligent routing systems. In the next article, you will learn the Map-Reduce pattern — processing large datasets by breaking them into chunks, processing in parallel, and merging results.
Continue to Map-Reduce Prompting Patterns to handle large-scale AI processing.
Module 4 — Chaining & Routing
Build multi-step prompt workflows with conditional logic.
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FAQ
What will I learn in this Prompt Orchestration guide?+
Master prompt routing techniques to dynamically select the right prompt based on input. Learn classification-based routing, confidence thresholds, and fallback strategies.