AI explainers for the curious
A practical reference library for anyone trying to understand AI, automation, governance, search and the systems around them without the hype.
Each explainer focuses on what the term means, where it changes real work, what can go wrong and what should be reviewed before it becomes normal practice.
AI foundations, models and capabilities
24 explainersStart here: the core language of AI, the models and agents behind it, and what they can do.
Tools, assistants and prompting
12 explainersThe main AI products and assistants in practical use, and how to instruct them well.
Workflow, adoption and value
18 explainersHow AI applies to real work: workflow redesign, adoption, proof of value and return.
Knowledge, data and integration
21 explainersHow organisations make knowledge, data and systems usable and AI-ready: RAG, search, APIs and integration.
Governance, risk and assurance
29 explainersThe practical operating controls for trustworthy AI: policies, risk, assurance, impact assessments, approval workflows and the frameworks teams implement.
Privacy, security and identity
28 explainersHow AI systems, data and people are protected: data boundaries, identity, access, security controls and misuse risks.
AI regulation: concepts, institutions and standards
30 explainersThe legal concepts, international frameworks and AI standards that shape how AI is regulated.
AI regulation: countries and regions
36 explainersHow AI is regulated jurisdiction by jurisdiction, from the EU and US to national and subnational regimes.
AI regulation: sectors and domains
8 explainersHow AI regulation applies in specific regulated domains such as healthcare, finance, employment and the public sector.
AI by business function and use case
7 explainersWhere AI shows up by team and use case, across operations, sales, finance, HR and more.
Search visibility, crawl and structured data
12 explainersHow search, answer engines and AI discovery change findability, and how crawl, indexing and structured data shape what they see.
AI delivery, operations and infrastructure
10 explainersHow AI and software systems are built, deployed, monitored and run: MLOps, LLMOps, DevOps, FinOps and infrastructure.
Engineering culture and software practice
39 explainersThe shared language, folklore and working habits of how software actually gets built.
Start with curiosity. Then choose the workflow.
AI becomes useful when the terms connect to real work: the knowledge people need, the systems they use, the risks they carry and the decisions they still need to own.