Generative AI Engineering with OpenAI, Anthropic

Master LLM integration, prompt design, and scalable AI app development using OpenAI and Anthropic APIs.

What you'll learn
- Design and Build Generative AI Applications using OpenAI (GPT) and Anthropic (Claude) models — from intelligent chatbots and copilots
- Master Prompt Engineering, Context Management, and Fine-Tuning to generate accurate, creative, and context-aware AI responses tailored to real-world use cases.
- Implement Retrieval-Augmented Generation (RAG) Pipelines by connecting vector databases such as Pinecone, FAISS, or Chroma, enabling AI systems.
- Integrate and Deploy AI Systems using modern frameworks like FastAPI, Flask, Streamlit, and React, building production-ready AI copilots and applications.
- Apply AI Safety, Cost Optimization, and Monitoring Techniques to ensure your systems are efficient, secure, and scalable, with guardrails for ethics
- Orchestrate Multi-Model Workflows combining OpenAI, Anthropic, and Mistral models for advanced reasoning, formatting, and performance efficiency.

Requirements
- Basic programming knowledge — familiarity with Python or JavaScript will help you follow along easily with hands-on examples.
- Fundamental understanding of AI or Machine Learning concepts — not mandatory, but helpful for grasping model behavior and architecture.
- Access to OpenAI and Anthropic APIs — you’ll learn how to obtain API keys and connect them to your applications.
- A computer with internet access — to build, test, and deploy projects using tools like FastAPI, Flask, Streamlit, or React.

Description
“This course contains the use of artificial intelligence”

Step into the future of innovation withGenerative AI Engineering: Build with OpenAI & Anthropic, ahands-on, lab-driven coursedesigned to help youmaster the art and science of building real-world AI applications. Whether you’re adeveloper, data engineer, researcher, or AI enthusiast, this course equips you with the technical depth and practical experience todesign, implement, and deploy intelligent systemspowered byLarge Language Models (LLMs)such asOpenAI’s GPTandAnthropic’s Claude.

You’ll begin by uncoveringhow LLMs think, reason, and generate, then dive into the engineering foundations that power them —prompt engineering,context management,embeddings, andfine-tuning. Through immersiveinteractive labs, you’ll experiment with APIs fromOpenAI, Anthropic, and Mistral, learning to control temperature, tokens, and reasoning depth to craft accurate, reliable, and domain-specific responses.

Beyond theory, this course emphasizesreal-world implementationthrough a full suite of12 practical labsand3 capstone projects:

Labs 1–7cover prompt chaining, API orchestration, latency benchmarking, and performance optimization.

Labs 8–12introduce advanced reasoning (Chain-of-Thought, self-reflection), safety guardrails, and deployment monitoring.

Projects 1–3guide you in building aTravel Itinerary Copilot, aCode Review Assistant, and aKnowledge-Aware RAG Copilotwith real-time tool integration.

You’ll also exploremulti-model orchestration,cost-efficient hybrid pipelines, andsecure deploymentusing frameworks likeFastAPI,Flask,Streamlit, andReact— transforming abstract AI capabilities intoproduction-grade applications.

By the end of this course, you’ll possess acomplete Generative AI engineering toolkit— spanningLLM design, evaluation, safety, and scaling— empowering you to turn innovative ideas into deployable, intelligent products.Become aGenerative AI Engineerwho bridgesimagination with implementation, building the next generation of smart, human-centered AI systems.

Who this course is for:
- A software engineer or developer eager to integrate OpenAI and Anthropic APIs into intelligent apps, copilots, and automation tools.
- A data scientist, ML engineer, or researcher looking to understand multi-model orchestration, RAG pipelines, and LLM-driven architectures.
- A tech entrepreneur or product builder who wants to create AI-powered startups, tools, or platforms using cost-effective, scalable methods.
- A student or beginner in AI who wants to gain hands-on skills in prompt engineering, context management, and AI deployment workflows.
- A professional in business, analytics, or design seeking to leverage AI copilots to enhance productivity, automate insights, and innovate processes.
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