Prompt Engineering & Generative AI for AI Engineers

About this course

Generative AI and Large Language Models (LLMs) are transforming how modern AI systems are built — and prompt engineering is now a core engineering skill, not just a trick.

This course is designed for AI engineers, ML practitioners, and developers who want to build real-world AI systems using prompt engineering, Python, machine learning, deep learning, LLMs, RAG, and modern GenAI tools.

Instead of treating prompt engineering as an isolated concept, you’ll learn how to integrate prompts into end-to-end AI workflows — from Python automation and data processing to LLM-powered applications, vector databases, and production-ready systems.

What You’ll Learn

In this course, you will:

• Understand prompt engineering fundamentals and mindset
• Use prompts to generate, debug, and document Python code
• Build ML and deep learning pipelines with prompt-assisted workflows
• Work with Transformers, LLMs, and HuggingFace models
• Design structured, few-shot, multi-step, and self-reflection prompts
• Build Retrieval-Augmented Generation (RAG) systems using vector databases
• Use FAISS, Chroma, and Pinecone for similarity search
• Apply prompt engineering to data cleaning, feature engineering, and evaluation
• Fine-tune models using LoRA and parameter-efficient techniques
• Build and deploy production-ready AI applications
• Apply MLOps practices with Git, Docker, and demo apps (Streamlit/Gradio)Create a professional AI portfolio with real projects

Hands-On Projects You’ll Build

This course is project-driven, not theory-heavy. You’ll build:

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