Prompt Engineering & Generative AI for AI Engineers

Prompt Engineering & Generative AI for AI Engineers

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

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What you'll learn

• Design and apply effective prompt engineering techniques (structured, few-shot, multi-step) for real AI engineering tasks
• Build end-to-end LLM applications using Python, HuggingFace Transformers, and modern Generative AI tools
• Create Retrieval-Augmented Generation (RAG) systems using vector databases such as FAISS, Chroma, or Pinecone
• Develop and deploy production-ready AI systems by combining prompt engineering, ML/DL models, and MLOps practices

Description

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

Who this course is for:

• Aspiring AI Engineers who want to build real-world AI systems using prompt engineering, LLMs, and Generative AI
• Machine Learning and Data Science practitioners looking to integrate LLMs, RAG, and prompt engineering into their workflows
• Python developers and software engineers who want to transition into AI engineering and GenAI application development
• Developers working with LLMs who want to design better prompts and build scalable, production-ready AI systems