Generative AI Engineering with OpenAI, Anthropic

“This course contains the use of artificial intelligence”Step into the future of innovation with Generative AI Engineering: Build with OpenAI Anthropic, a hands-on, lab-driven course designed to help you master the art and science of building real-world AI applications. Whether you’re a developer, data engineer, researcher, or AI enthusiast, this course equips you with the technical depth and practical experience to design, implement, and deploy intelligent systems powered by Large Language Models (LLMs) such as OpenAI’s GPT and Anthropic’s Claude.You’ll begin by uncovering how LLMs think, reason, and generate, then dive into the engineering foundations that power them — prompt engineering, context management, embeddings, and fine-tuning. Through immersive interactive labs, you’ll experiment with APIs from OpenAI, Anthropic, and Mistral, learning to control temperature, tokens, and reasoning depth to craft accurate, reliable, and domain-specific responses.Beyond theory, this course emphasizes real-world implementation through a full suite of 12 practical labs and 3 capstone projects: Labs 1–7 cover prompt chaining, API orchestration, latency benchmarking, and performance optimization. Labs 8–12 introduce advanced reasoning (Chain-of-Thought, self-reflection), safety guardrails, and deployment monitoring. Projects 1–3 guide you in building a Travel Itinerary Copilot, a Code Review Assistant, and a Knowledge-Aware RAG Copilot with real-time tool integration.You’ll also explore multi-model orchestration, cost-efficient hybrid pipelines, and secure deployment using frameworks like FastAPI, Flask, Streamlit, and React — transforming abstract AI capabilities into production-grade applications.By the end of this course, you’ll possess a complete Generative AI engineering toolkit — spanning LLM design, evaluation, safety, and scaling — empowering you to turn innovative ideas into deployable, intelligent products.Become a Generative AI Engineer who bridges imagination with implementation, building the next generation of smart, human-centered AI systems.