Introduction
Multi‑model AI systems and demo final project
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1. The Single-Model Trap
The single model trap: Why AI products fail at scale
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Build a chatbot with Claude
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Find chatbot failures: Cost, latency, and verification gaps
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2. Multi-Model Architecture
Designing multi-model workflows for scale
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Choosing the right Claude model
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Fast intent classification with Claude Haiku
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Smart routing: Directing requests to the right model
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Confidence-based fallbacks for edge cases
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3. Guardrails — Security Without Cost
Guardrails first: Zero-cost safety before model calls
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Detect PII with local regex: Emails and phone numbers
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Block unsafe prompts: Injection and jailbreak detection
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4. Evaluation Beyond Accuracy
Beyond accuracy: Balancing latency, cost, and risk
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Build AI metrics: Timing, tokens, and routing
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Implement status thresholds (red/amber/green)
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Generate evaluation reports with route‑level insights
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5. Module 5: Putting It All Together
Run the full stack: Debug view and metrics dashboard
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Review route distribution and cost breakdown
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Production practices: Logging, human-in-the-loop, and monitoring
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6. Conclusion
What you built and where to go next
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