Generative AI Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This comprehensive Generative AI course is designed for developers seeking mastery in building deployable LLM-based applications. Structured across five core phases and a capstone project, the program spans approximately 110 hours of hands-on learning. You'll progress from Python and NLP fundamentals through LLM architecture, prompt engineering, and real-world application development using LangChain and Jupyter. The curriculum blends self-paced labs with live-led instruction, culminating in a full-stack project that demonstrates your readiness for roles in generative AI. Career support, expert feedback, and a certificate of completion ensure strong job market alignment.
Module 1: AI & Python Fundamentals
Estimated time: 20 hours
- Core Python scripting for AI applications
- Data handling and preprocessing techniques
- Introduction to machine learning and NLP basics
- Hands-on data cleaning, visualization, and baseline modeling
Module 2: LLMs & Generative AI Concepts
Estimated time: 20 hours
- Understanding LLM architectures and model internals
- Fine-tuning strategies and transfer learning
- Evaluation metrics for generative models
- Hands-on prompt experiments and model comparisons
Module 3: Prompt Engineering Mastery
Estimated time: 25 hours
- Zero-shot, one-shot, and few-shot prompting techniques
- Chain-of-thought reasoning and advanced prompting patterns
- Prompt testing, debugging, and iterative refinement
- Designing reusable prompt templates and workflows
Module 4: Application Development
Estimated time: 25 hours
- Building a code-review assistant with LLMs
- Developing RAG (Retrieval-Augmented Generation) systems
- Creating API-interactive bots using LangChain
- Financial report analysis with Jupyter notebooks
Module 5: Capstone Project
Estimated time: 20 hours
- Design and implement a full-stack LLM application
- Integrate prompt engineering with application logic
- Deploy a production-ready solution with live demo
Prerequisites
- Basic programming experience in Python
- Familiarity with data structures and functions
- Interest in AI, NLP, or software development
What You'll Be Able to Do After
- Build and deploy production-grade LLM applications
- Design and optimize effective prompting strategies
- Implement RAG systems for knowledge-intensive tasks
- Create intelligent bots integrated with external APIs
- Demonstrate expertise through a portfolio-ready capstone project