GenerativeAICourse

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GenerativeAICourse is a Research & Education tool that provides a comprehensive course on generative AI, from fundamentals to building production-grade applications. It includes hands-on tutorials for chatbots, RAG, and AI agents.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is GenerativeAICourse?

GenerativeAICourse offers a comprehensive and practical course designed to teach generative AI from first principles. It covers the invention of AI, the rise of large language models (LLMs), and moves into hands-on tutorials. Users will learn to build basic chatbots, implement Retrieval-Augmented Generation (RAG), create AI agents, explore Model Context Protocol (MCP), and understand the requirements for building production-grade AI applications. The course emphasizes practical relevance, avoiding jargon and overly academic content, making AI engineering accessible for those looking to build real-world AI solutions that scale. It highlights key differences between traditional ML and AI engineering, focusing on adapting models rather than just training them.

Best used for

Ideal for educators and learners who need to grasp generative AI fundamentals, build practical AI applications, and understand the shift from traditional ML to AI engineering. Especially valuable for those seeking hands-on experience with LLMs, RAG, and AI agents without excessive jargon.

Common actions

learn generative AI

build AI applications

understand LLMs

practice prompt engineering

implement RAG

deepfakeautomated workflowlow-code/no-codeworkflowsopen-sourcecollaboration"AI Agents"github copilotface swapping

Capabilities

Key features

Deploy local LLMs
Build AI chatbots
Implement RAG
Create AI agents
Prompt engineering
Understand LLMOps

Target Audience

professorteachertutor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What specific topics are covered in the Generative AI Course?

The course covers deploying local LLMs, building end-to-end AI chatbots, prompt engineering, defensive prompting, Retrieval-Augmented Generation (RAG), AI Agents, Model Context Protocol (MCP), and LLMOps. It starts from AI fundamentals and progresses to advanced application building.

What are the prerequisites for taking this course?

The course starts from absolute first principles, so it's designed to be accessible. However, familiarity with basic programming concepts, especially Python, and comfort with a development environment like VS Code would be beneficial for the hands-on labs.

How does this course differ from other AI courses?

This course focuses on practical AI engineering, emphasizing adapting models rather than just training them. It avoids jargon and academic fluff, providing hands-on tutorials for building real-world, scalable AI solutions, which is often lacking in other offerings.

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