AI Foundations
Core ideas, terms, and mental models for understanding how AI systems work.
AI knowledge literacy
Plain-English explanations of AI ideas, tools, risks, and workflows for people building judgment around modern AI.
A practical explanation of AI assistants as products that help with tasks, without confusing them with models or agents.
A plain-English foundation for understanding AI systems, outputs, and the judgment needed around them.
A practical explanation of the token capacity a model can use during one request, and why it is not memory.
A practical guide to text embeddings for search and RAG, including what vector similarity can and cannot prove.
A practical guide to recognizing generated AI content that sounds plausible but is false or unsupported.
A practical guide to designing human review, authority, and escalation into AI-assisted work.
A plain-English explanation of what LLMs are, how they generate text, and where users need verification.
A practical explanation of how models learn from data, generalize to new cases, and fail when the pattern is weak.
A practical guide to prompts as task instructions and context, without treating prompt craft as proof of truth.
A practical explanation of how RAG combines retrieval with generation, and why grounding still needs verification.
A practical guide to recognizing information that needs extra care before it is shared with AI tools.
A practical guide to checking whether a source exists, is relevant, and supports the claim attached to it.
Core ideas, terms, and mental models for understanding how AI systems work.
How text, image, audio, video, and multimodal AI tools create and transform content.
Practical ways AI is used across products, services, operations, and everyday tasks.
Safety, reliability, limitations, evaluation, and responsible use for ordinary users.
AI concepts for workplace productivity, collaboration, judgment, and process design.
Concepts for creating AI-assisted workflows, prototypes, products, and systems.