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AI promises transformative features—from smart search to dynamic assistants—but shipping it reliably is harder than tutorials suggest. How do you embed LLMs without latency spikes? When should you use a vector DB vs. fine-tuning? Can you avoid vendor lock-in? This code-heavy, no-fluff workshop covers: ✅ Seamless Integration: Add AI to existing Python/JS apps (Django, Flask, Node) without full rewrites. ✅ Architecture Patterns: Hybrid pipelines (e.g., LLM caching with Redis, cost-efficient RAG with pgvector/Chroma). ✅ Tools You’ll Actually Use: Django for AI endpoints. ✅ Production Gotchas: Rate limiting, privacy-preserving embeddings, and fallback strategies for when APIs fail.