AI Design Patterns Explained: The Complete Framework for Production AI Systems скачать в хорошем качестве

AI Design Patterns Explained: The Complete Framework for Production AI Systems

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. 📬 Subscribe to Nanobits for weekly breakdowns of what's actually working in AI products—no hype, just practical insights: https://nanobits.beehiiv.com/ #AIEngineering #MachineLearning #LLM #AIPatterns #ProductionAI #RAG #PromptEngineering #AIDesignPatterns #SoftwareEngineering #AIProductManagement