1️⃣ Routing System Vision Build an intelligent routing system that dynamically selects: Best prompt template Most efficient AI model Optimal workflow path Goals: Reduce cost by using cheaper models when possible 💰 Improve output quality using specialized prompts 🎯 Increase speed with efficient routing ⚡ --- 2️⃣ Task Classification Layer System first analyzes incoming request using NLP classifier Classification Rules: User Intent Detection Informational → Q&A Creative → Content Writing Logical → Analysis Task Type Detection Blog / Ad Copy → Writing Fact-based → Q&A Data / Strategy → Analysis Complexity Detection Simple → Short answers Medium → Structured output Complex → Multi-step reasoning --- 3️⃣ Prompt Selection Strategy Maintain a Prompt Library Database Prompt Mapping: Writing Tasks → AIDA / PAS frameworks Q&A Tasks → Direct Answer Prompt Analysis Tasks → Step-by-step reasoning prompt Use metadata tags: task_type tone output_format --- 4️⃣ Model Selection Logic Select model based on complexity + cost Rules: Simple Tasks Use Open Model (low cost) Medium Tasks Use GPT (balanced) Complex Tasks Use Advanced GPT (high reasoning) --- 5️⃣ Routing Decision Engine Core logic connects: task → prompt → model Decision Flow: 1. Detect task type 2. Detect complexity 3. Match prompt template 4. Select model 5. Execute request Example: Blog Writing → AIDA Prompt → GPT Simple Q&A → Direct Prompt → Open Model --- 6️⃣ Multi-Step Workflow Routing Complex tasks are split into stages: Workflow Example: 1. Input Understanding 2. Task Breakdown 3. Subtask Routing 4. Output Aggregation Example: Market Analysis Task: Step 1 → Data Extraction (Open Model) Step 2 → Insight Generation (GPT) Step 3 → Final Summary (GPT) --- 7️⃣ Cost Optimization Layer Apply cost-saving strategies: Route 80% simple tasks → cheap models Cache repeated queries Limit token usage Use shorter prompts when possible --- 8️⃣ Performance Monitoring System Track key metrics: Response Quality Score Latency (response time) Cost per request Store logs for continuous improvement 📊 --- 9️⃣ Fallback & Error Handling If model fails: Retry with alternative model Switch to backup prompt Simplify request Fallback Example: Open Model fails → Switch to GPT Complex output fails → Use simplified prompt --- 🔟 Prompt Routing Blueprint Core Components: Task Classifier Prompt Library Model Selector Routing Engine Workflow Manager Cost Optimizer Monitoring System Fallback Handler --- ✅ Final System Outcome Intelligent task routing Optimized cost-performance balance Scalable AI system for SaaS growth 🚀 --- If you want next level, I can give you: Real no-code tools stack Or how to build this system step-by-step (free tools)