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Tag guide
This tag gathers mini classes around one specific topic. Start with the newest post, then use categories and related reading for context.
Anthropic says Claude now handles most internal analytics questions, but the key is not simply a smarter model. Teams first need fixed data sources, metric definitions, query steps, and review rules.
The UK facial age estimation case shows why high-risk AI needs human override, appeal, and pause rules before model accuracy becomes the focus.
People can use AI and still worry it is moving too quickly. Before speeding up adoption, check control, personal-data risk, and recovery paths, then decide which tasks can advance and which should slow down first.
AI memory can reduce repeated setup, but it can also bring stale context into new tasks. Use green, yellow, and red labels to decide what stays, what needs confirmation, and what should pause before important judgment.
Notion and Anthropic service disruptions are a reminder that AI features become workflow dependencies. The practical question is what your team can still deliver when AI is unavailable.
Bad examples, outdated policies, and counterexamples are not safe just because you add “do not believe this.” Decide the risk first, then add labels, filtering, tests, and output checks.