Claude Code: Designing Multi-Model AI Systems
2h 5mIntermediate2026-04-27
Authors
Isil Berkun
Data Scientist at Intel Corp.
Course details
Use multi-modal AI systems with Claude Code to transform the way you approach AI developments. Dr. Isil Berkun, AI educator and founder of DigiFab.AI, shows you how to implement effective routing, fallback, and guardrail strategies. Explore ways to optimize decision quality, job robustness, and safety through a comprehensive evaluation pipeline that examines latency, cost, and routing outcomes. Build the practical skills you need to create resilient and maintainable AI systems, through hands-on exercises. Perfect for applied ML engineers, AI-focused individual contributors, and technical leaders, this course offers insights on mapping tasks to proper models and efficiently operating AI systems with human-in-the-loop reviews and incident response. By the course's end, you'll have completed a Claude Code project showcasing a scalable, production-ready AI system.
Learning objectives
Design a multi‑model AI system architecture by mapping tasks to appropriate models and tools, demonstrated through an architecture diagram and rationale.
Implement routing, fallbacks, and guardrails using Claude Code to handle uncertainty, failures, and safety constraints.
Evaluate AI system decision quality beyond accuracy by building an evaluation pipeline measuring correctness, safety, latency, cost, and routing outcomes.
Operate and improve an AI system using observability, human‑in‑the‑loop review queues, and incident response runbooks.
Learning objectives
Design a multi‑model AI system architecture by mapping tasks to appropriate models and tools, demonstrated through an architecture diagram and rationale.
Implement routing, fallbacks, and guardrails using Claude Code to handle uncertainty, failures, and safety constraints.
Evaluate AI system decision quality beyond accuracy by building an evaluation pipeline measuring correctness, safety, latency, cost, and routing outcomes.
Operate and improve an AI system using observability, human‑in‑the‑loop review queues, and incident response runbooks.
Concepts
Introduction
- Multi model AI systems and demo final project
The Single-Model Trap
- The single model trap - Why AI products fail at scale
- Build a chatbot with Claude
- Find chatbot failures - Cost, latency, and verification gaps
Multi-Model Architecture
- Designing multi-model workflows for scale
- Choosing the right Claude model
- Fast intent classification with Claude Haiku
- Smart routing - Directing requests to the right model
- Confidence-based fallbacks for edge cases
Guardrails Security Without Cost
- Guardrails first - Zero-cost safety before model calls
- Detect PII with local regex - Emails and phone numbers
- Block unsafe prompts - Injection and jailbreak detection
Evaluation Beyond Accuracy
- Beyond accuracy - Balancing latency, cost, and risk
- Build AI metrics - Timing, tokens, and routing
- Implement status thresholds (red amber green)
- Generate evaluation reports with route level insights
Module 5 - Putting It All Together
- Run the full stack - Debug view and metrics dashboard
- Review route distribution and cost breakdown
- Production practices - Logging, human-in-the-loop, and monitoring
Conclusion
- What you built and where to go next