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Generative AI Engineering

Build LLM-powered applications. Master Prompt Engineering, RAG (Retrieval Augmented Generation), and Vector Databases to create smart agents.

The AI revolution is here. This course moves beyond 'chatting' with ChatGPT to engineering applications *on top* of Large Language Models. You will learn the art of advanced Prompt Engineering to get consistent outputs. Dive into building RAG systems that allow AI to answer questions from your own private data using Vector Databases like Pinecone. We cover LangChain for chaining logic and building autonomous agents. This is the cutting edge of software development today.

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⏱️ 5-Minute Lessons (Bite-sized learning)
🚀 21-Lesson Path (Independent modules)
📱 Mobile Friendly (Learn anywhere)
AI Research Lab
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Complete Course Syllabus

  • 1
    LLM Fundamentals
    How transformers work and interacting via APIs.
  • 2
    Prompt Engineering
    Zero-shot, few-shot, and chain-of-thought strategies.
  • 3
    Vector Databases
    Embeddings, semantic search, and long-term memory.
  • 4
    RAG Implementation
    Retrieving context to ground AI answers in facts.
  • 5
    Agents & Tools
    Giving AI access to calculators, search, and APIs.

Estimated completion time: 21 lessons • Self-paced learning • Lifetime access

Career Outlook

Estimated Salary
$130k - $180k

Career Paths

AI Engineer $130k-$180k
LLM Developer $140k-$190k
AI Solutions Architect $150k-$200k

What You Will Learn

Build RAG applications to chat with your own PDF documents
Master advanced Prompt Engineering techniques for consistent results
Implement Vector Databases for semantic search and memory retrieval
Create autonomous agents using LangChain to perform multi-step tasks
Integrate OpenAI and open-source models via API endpoints

Skills You Will Gain

Prompt Engineering LangChain Vector Databases RAG Architecture Python API Integration

Who Is This For

Python Developers
Data Engineers
AI Enthusiasts

Prerequisites

Python proficiency
Basic API knowledge

Generative AI Engineering FAQs

Training models?

No, we focus on the Application Layer (using models).

Math required?

Minimal; focus is on architecture and logic.

Expensive APIs?

We teach cost estimation; Open Source options exist.

Job market?

Explosive demand for engineers who know this stack.

Need more info?
All Reork courses are 100% free, self-paced, and fully accessible on mobile devices. No prior experience is required. Read our full Platform FAQ.

Tech Stack & Tools

LangChain
OpenAI API
Pinecone
Hugging Face
Streamlit

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Your Learning Path

Master This Topic, Step by Step

Go from your first step to master level. Three simple steps, all about Generative AI Engineering.

Currently studying: Generative AI Engineering
Step 1 · The Basics

Generative AI Engineering Basics

Simple words. Clear examples. The friendly way to start Generative AI Engineering.

5 min Free to explore
Generative AI Engineering for Beginners
Start with the basics — just 5 easy minutes
Step 2 · Next Level

Advanced Generative AI Engineering

This is where Generative AI Engineering gets exciting — deeper skills, real results.

5 min Free to explore
Advanced Generative AI Engineering Course
Move up to the advanced level — 5 min
Step 3 · Master With AI

Master Generative AI Engineering with AI

Masters work smart. Ollama + Local LLMs can run powerful AI models on your own machine — in minutes, not hours.

5 min Free to explore
Local AI Models
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Start with Step 1. Each step builds on the one before it.
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