AI Concepts

AI Concepts covers the foundational technologies behind modern AI: machine learning, large language models, prompt engineering, agentic AI, and responsible AI governance.

Four Areas of AI Knowledge

AI concepts are organized into four sub-sections based on where the technology sits in the adoption curve and how teams encounter it at work.

Section	What It Covers	Who It Is For
Fundamentals	Machine learning, NLP, computer vision, types of AI	Anyone new to AI who needs the basics
Generative AI	LLMs, prompt engineering, RAG, fine-tuning, vibe coding	Teams actively using ChatGPT, Claude, or similar tools
Agentic AI	AI agents, MCP, A2A, chatbots, copilots, automation	Teams building or evaluating agent workflows
Responsible AI	Governance, ethics, bias, safety, enterprise deployment	Leaders making AI adoption decisions

Browse AI Concepts

Agentic AI Agentic AI covers autonomous systems that plan, execute tasks, use tools, and make decisions with minimal human oversight. Includes AI agents, MCP servers, the A2A protocol, chatbots, copilots, and automation frameworks.

AI Fundamentals AI Fundamentals covers the core technologies that power modern AI systems, from machine learning and neural networks to natural language processing and computer vision.

Generative AI Generative AI covers the technologies behind tools like ChatGPT, Claude, and Gemini. Includes large language models, prompt engineering, retrieval-augmented generation, and emerging approaches like vibe coding.

Responsible AI Responsible AI covers the governance, ethics, and safety frameworks teams need to deploy AI without introducing risk. Includes governance frameworks, enterprise AI strategy, bias mitigation, and AI literacy programs.

Common Questions About AI Concepts

What is the difference between AI and machine learning?

AI is the broad field of making machines perform tasks that typically require human intelligence. Machine learning is a subset of AI where systems learn from data rather than being explicitly programmed. All machine learning is AI, but not all AI is machine learning.

What is generative AI?

Generative AI refers to AI systems that create new content including text, images, code, and audio based on patterns learned from training data. ChatGPT, Claude, Gemini, and DALL-E are examples. The technology is built on large language models (LLMs) trained on massive text datasets.

What is agentic AI?

Agentic AI describes systems that can plan, execute multi-step tasks, use tools, and make decisions with minimal human oversight. Unlike chatbots that respond to one prompt at a time, agents can break down goals into subtasks, call APIs, search the web, and iterate on their own output.

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