I Shipped AI Features That Broke. Here's What I Learned About AI Design Patterns

Описание: 78% of companies use AI daily, but most AI features break in production. Here's why and the engineering framework that fixes it.

In this video, I break down the complete set of AI-native design patterns that turn unstable prototypes into reliable production systems. If you've shipped AI features that hallucinated, drifted, or became unpredictably slow, this is for you.

🎯 What You'll Learn:
Why traditional software patterns don't work for AI systems
The 4 Behavior Patterns that make LLMs predictable
Retrieval Patterns that ground models in real data (RAG, Memory, Ranking)
Governance Patterns that keep AI safe and compliant in production
How these patterns work together in real production systems

Here is how we cover them:
Why AI breaks in production?
Traditional vs AI systems
Behavior Patterns (Structured Prompting, N-Shot, Context Framing)
Prompt Versioning & Experimentation
Retrieval Patterns (RAG, Memory, Freshness)
Governance Patterns (Guardrails, Tracing, Feedback)
How patterns compose in real systems
Next steps for builders and leaders

This is about engineering discipline for AI systems. Whether you're a developer debugging hallucinations or a product manager looking to create your next AI feature or a leader trying to scale AI products, these patterns are how you close the gap between demos and production.

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