How AI Workflows Really Work (Part 2/2)

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Real AI products aren't built with one giant prompt. 🧠⚙️ (Part 2/2) They're built with workflow patterns. In Part 2 we're covering the advanced 3: 1️⃣ Parallelization → Sectioning (split subtasks, run in parallel) + Voting (same task, multiple models, take consensus). 2️⃣ Orchestrator-Workers → A central LLM dynamically decides what subtasks are needed and delegates to workers. 3️⃣ Evaluator-Optimizer → Generator LLM + Critic LLM loop until output is good enough. The common thread: your code controls the flow. The LLM just fills in the intelligence. That's what makes AI systems predictable, debuggable, and production-ready. 🚀 🎬 Part 1 (chaining + routing) is on the profile. 💡 Save + share with a dev who's building AI stuff. 👉 Follow for more AI engineering shorts. . . . #AIWorkflows #LLM #Orchestrator #EvaluatorOptimizer #Parallelization #PromptEngineering #AIEngineering #GenAI #MachineLearning #AIAgents #LLMOps #AIDevelopment #DeveloperTips #CodingReels #TechExplained #ArtificialIntelligence #LearnAI #AISeries #AITutorial #KodeKloud #DevOps #AIForDevelopers