Generative AI Engineering with OpenAI, Anthropic

Name: Generative AI Engineering with OpenAI, Anthropic
Availability: InStock
Rating: 4.00 (791 reviews)

4.00

7,910 students

10h 35m

Updated Mar 2026

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.

Course Description

“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.

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.