Prompt Engineering & Generative AI for AI Engineers

Build LLM, RAG & AI Automation Systems with Python, Transformers, Vector Databases & Real Projects

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:

• Prompt-assisted Python automation scripts

• Data analysis & visualization workflows using prompts

• Machine learning & deep learning models

• NLP systems like sentiment analyzers

• Computer vision classifiers using CNNs and transfer learning

• LLM applications using HuggingFace Transformers

• A RAG-based AI assistant using vector databases

• Prompt libraries for reusable AI workflows

• End-to-end GenAI systems ready for deployment

The final section focuses on capstone portfolio projects, such as:

• AI medical assistant

• AI resume analyzer & job matcher

• AI customer support agent

• Multimodal AI systems (text + images)

Why This Course Is Different

Most courses either:

• Teach prompt engineering in isolation, or

• Teach AI/ML without showing how LLMs and prompts fit into real systems

This course bridges that gap.

You’ll learn:

• When to use prompts vs code

• How prompts improve productivity for AI engineers

• How to combine LLMs, ML models, vector databases, and automation

• How modern AI systems are actually built in practice

Who This Course Is For

This course is ideal for:

• Aspiring AI Engineers

• Machine Learning & Deep Learning practitioners

• Python developers moving into Generative AI

• Data scientists working with LLMs

• Software engineers building AI-powered products

Prerequisites

• Basic Python knowledge is helpful (a fast-track Python section is included)

• No prior experience with LLMs or prompt engineering is required

By the End of This Course

You’ll be able to:

• Design effective prompts for real engineering tasks

• Build LLM-powered AI systems end to end

• Confidently work with modern GenAI tools

• Showcase multiple AI projects in your portfolio

• Apply prompt engineering as a professional AI engineering skill