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AI Concepts
Clear explanations of the ideas, risks, and building blocks behind practical AI work.
Each concept states what it means, why it matters, where it can fail, and which sources support the explanation.
Choose a starting point
Browse by concept family.
Categories organize the library; every article can also connect you to related practice, reviewed evidence, and tools.
AI Foundations
Core ideas, terms, and mental models for understanding how AI systems work.
3 conceptsGenerative AI
How text, image, audio, video, and multimodal AI tools create and transform content.
1 conceptsAI Applications
Practical ways AI is used across products, services, operations, and everyday tasks.
3 conceptsAI Literacy & Safety
Safety, reliability, limitations, evaluation, and responsible use for ordinary users.
1 conceptsAI at Work
AI concepts for workplace productivity, collaboration, judgment, and process design.
2 conceptsBuilding with AI
Concepts for creating AI-assisted workflows, prototypes, products, and systems.
All concepts
Start with the question you need to understand.
This is a maintained learning library, not a glossary of every AI term.
AI Assistant
A practical explanation of AI assistants as products that help with tasks, without confusing them with models or agents.
Reviewed 2026-08-04 →ai foundationsArtificial Intelligence
A plain-English foundation for understanding AI systems, outputs, and the judgment needed around them.
Reviewed 2026-08-04 →generative aiContext Window
A practical explanation of the token capacity a model can use during one request, and why it is not memory.
Reviewed 2026-08-04 →building with aiEmbedding
A practical guide to text embeddings for search and RAG, including what vector similarity can and cannot prove.
Reviewed 2026-08-04 →ai literacy safetyHallucination
A practical guide to recognizing generated AI content that sounds plausible but is false or unsupported.
Reviewed 2026-08-04 →ai at workHuman Oversight
A practical guide to designing human review, authority, and escalation into AI-assisted work.
Reviewed 2026-08-04 →generative aiLarge Language Model
A plain-English explanation of what LLMs are, how they generate text, and where users need verification.
Reviewed 2026-08-04 →ai foundationsMachine Learning
A practical explanation of how models learn from data, generalize to new cases, and fail when the pattern is weak.
Reviewed 2026-08-04 →generative aiPrompt
A practical guide to prompts as task instructions and context, without treating prompt craft as proof of truth.
Reviewed 2026-08-04 →building with aiRetrieval-Augmented Generation
A practical explanation of how RAG combines retrieval with generation, and why grounding still needs verification.
Reviewed 2026-08-04 →ai literacy safetySensitive Data
A practical guide to recognizing information that needs extra care before it is shared with AI tools.
Reviewed 2026-08-04 →ai literacy safetySource Verification
A practical guide to checking whether a source exists, is relevant, and supports the claim attached to it.
Reviewed 2026-08-04 →