Prompt Engineering & LLM Production Master the practical craft of prompt engineering and learn how to design, test, and deploy reliable AI-driven workflows that power real products. This immersive, hands-on course walks you from first principles to production-ready systems, with a focus on reproducible practices, measurable improvements, and real-world integrations. Whether you want to build smarter content pipelines, automated customer support, or code-generation assistants, this course teaches the exact skills, patterns, and guardrails you’ll use every day as an AI prompt engineering practitioner.What this course is (straight, no fluff)This is a pragmatic, exercise-first course on prompt engineering for people who want results — not just theory. You’ll learn how to craft prompts that produce consistent outputs, control model behavior (temperature, top_p, tokens, penalties), evaluate and A/B-test prompt variants, chain prompts into multi-step pipelines, and move from manual experimentation into reliable automation using AP Is and tooling like LangChain and Prompt Layer. The course emphasizes safety, cost-efficiency, and measurable outcomes so you can deploy prompt-based features in production with confidence.Key skills you’ll walk away with Expert-level chatgpt prompt engineering techniques: system/user/assistant role design, few-shot teaching, and format enforcement.Robust experiment practices: hypothesis design, A/B testing, logging, and quantitative metrics (accuracy, F1 proxies, user satisfaction).Production patterns: prompt chaining, map-reduce strategies, validation layers, caching, and failover/human-in-the-loop design.Cost & performance optimization: token compression, reuse strategies, and measurable latency/cost tradeoffs.Safety & compliance: anti-hallucination pattern
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