This course provides a structured pathway to becoming a Generative AI and Prompt Engineering professional.
You’ll learn how modern Large Language Models (LLMs) work, how to craft high-quality prompts, build AI-powered applications, implement RAG (Retrieval-Augmented Generation), and fine-tune custom models.

You will work with popular GenAI tools and frameworks such as LangChain, LlamaIndex, Hugging Face, and OpenAI APIs.
By the end of the course, learners will be able to design AI agents, build domain-specific chatbots, and deploy GenAI applications for real use cases.

Learning Outcomes

1. Generative AI Foundations

• Understand what Generative AI is

• LLM architecture basics

• Tokens, embeddings & context window

• Use cases of GenAI across industries

2. Prompt Engineering Fundamentals

• Zero-shot, one-shot & few-shot prompting

• Instruction-based prompting

• Role prompting & context framing

• Prompt structuring techniques

3. Advanced Prompt Engineering

• Chain-of-thought prompting

• Self-consistency & reasoning prompts

• Prompt optimization & refinement

• Using templates & frameworks

4. Multimodal Prompting

• Image, text, audio prompting

• Vision+Language models basics

• Generating images & multimodal outputs

5. LLM Tools & Frameworks

• LangChain basics

• LlamaIndex workflows

• OpenAI & Gemini API usage

• Hugging Face model handling

6. Retrieval-Augmented Generation (RAG)

• Intro to RAG pipelines

• Embeddings & vector databases

• Indexing & document retrieval

• Building RAG-based chatbots

7. Fine-Tuning & Custom Models

• LoRA & QLoRA

• Dataset preparation

• Domain-specific fine-tuning

• Model optimization

8. Building AI Agents

• Agent architecture

• Tool usage & function calling

• Memory & planning systems

• Deploying agents in real apps

9. Responsible AI

• AI ethics & safety

• Bias & hallucination control

• Secure GenAI application design

10. Capstone Project

Choose one:

• AI Chatbot for a business

• RAG-based search assistant

• Domain-specific LLM agent

• Custom fine-tuned model demo