Elective
Gen AI + Prompt Engineering
From LLM fundamentals to RAG, agents, and multimodal AI - 24 sessions of hands-on generative AI skills.
Duration
12 weeks, 24 sessions
Audience
Developers, analysts, and professionals who want practical, production-ready GenAI skills.
Prerequisites
Basic Python recommended; any LLM account (Azure OpenAI / OpenAI).
Venue
Wed 19:00-21:00, Room 1 (B414). Guest lecture by Xurshidbek Kurbanov on odd weeks, within 19:00-21:00 (up to two hours); tutor-led practice on the other weeks.
Tools
Python Google Colab / Jupyter OpenAI API LangChain LangGraph GitHub
Learning Outcomes
- ✓ Understand how LLMs work and build chatbots with the OpenAI API
- ✓ Apply RAG pipelines using LangChain and LangGraph for document-based AI
- ✓ Master prompt engineering patterns: zero-shot, few-shot, chain-of-thought, ReAct
- ✓ Generate, summarize, extract, and analyze data with LLMs
- ✓ Build multimodal AI applications and complete a mini-project from planning to showcase
Curriculum
12-Week Curriculum
| Week | Session A | Session B |
|---|---|---|
| 1 | What Can We Do with LLMs | Environment Setup and Getting Started with GPT API |
| 2 | Building a Chatbot with OpenAI’s API | AI Researcher for Summarizing Documents and Papers |
| 3 | RAG, LangChain, LangGraph Fundamentals | RAG in Practice |
| 4 | LangChain Simplified Demo | LangGraph Simplified Demo |
| 5 | Zero-shot & Few-shot Prompting | Chain-of-Thought & Self-Consistency |
| 6 | ReAct Prompting (Reason + Act) | Iterative Refinement & Prompt Debugging |
| 7 | Summarization & Structured Extraction | Handling Long Text |
| 8 | Creative AI Writing | Interactive Assistants (Memory Simulation) |
| 9 | Data Analysis in LLMs | Code Generation & Debugging with LLMs |
| 10 | Academic Writing & Paraphrasing | Business Applications of LLMs |
| 11 | Multimodal AI in LLM | Mini Project Planning & Data Preparation |
| 12 | Mini Project Implementation | Mini Project Showcase |
Assessment
Attendance 40%
Expert Lecture Participation 30%
Exams (Midterm + Final) 30%
Completion requires ≥75% attendance (9 of 12 weeks), grade D or above in both enrolled courses, and taking the midterm TEST (Week 7, online) and final TEST (Week 12, online).
Grades: A · B · C · D · F
Tools & Platforms
Python Google Colab / Jupyter OpenAI API LangChain LangGraph GitHub