From tokens to agents, the atlas of AI.
LLM, RAG, MCP, embedding, fine-tuning, machine learning… Every concept of modern AI in one place, with deep guides and real-world examples.
Featured terms
The most searched and most misunderstood concepts in modern AI.
Artificial Intelligence
The broad field of building machines that mimic human-like learning, reasoning, and decision-making.
Machine Learning
Letting computers learn patterns from data instead of being explicitly programmed.
LLM
A massive AI model trained on huge text corpora that learns to predict the next token and can hold conversations.
Transformer
The neural-network architecture behind modern LLMs that learns how much each word should attend to every other word.
Embedding
A vector representation that captures the meaning of text, images, or audio.
RAG
Query → document retrieval → LLM context injection → answer generation.
MCP
An open protocol that lets AI applications connect to external data sources and tools in a standardized way.
AI Agent
An autonomous system that uses an LLM as its brain to call tools, plan, and reach a goal step by step.
Prompt
The input text you give an LLM — its quality largely determines the quality of the answer.
Guides
Practical, step-by-step guides that go beyond a single term — built around real-world examples.
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AI Governance for Companies: A Practical Starter Playbook
A workable AI governance setup for small and mid-sized companies starting with LLMs and agents: the October 2026 map of the EU AI Act, GDPR, KVKK, NIST AI RMF and ISO/IEC 42001, an AI inventory template, risk classification, policies, technical controls, a vendor checklist, a RACI table and a 30/60/90-day plan.
Governance EU AI Act Compliance -
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Anatomy of an AI Harness: What Surrounds the Model
The layer around the model in tools like Claude Code, Codex CLI, Cursor and Gemini CLI: agent loop, tools, context management, subagents, skills, hooks, permissions, sandboxing, memory, and a sub-70-line harness you can build yourself.
Agents Claude Code Context Engineering -
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Do you need AI, ML, or just an LLM?
How to decide between AI, classical machine learning, and a large language model for a new feature — a decision flow with real scenarios.
Decision AI ML -
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Build Your Own MCP Server
Write an MCP server from scratch that adds capabilities to your own AI assistant — a local task-manager scenario, TypeScript and Python examples, works with any MCP-compatible host.
MCP Server SDK
Calculators & tools
From token cost to GPU sizing — practical answers to the questions you face when shipping AI.