AI Data & Analysis Guides
Cleaning, querying and explaining data with AI, and checking what it tells you.
Analyse a spreadsheet with AI and check its work
AI data analysis is genuinely useful and silently wrong often enough that an unchecked number should nev...
Read guideBuild a RAG pipeline over your own documents
The demo takes an afternoon. Making retrieval return the right passage on the questions people actually...
Read guideWrite SQL with AI against a schema it has never seen
A model will write syntactically perfect SQL against a schema it is guessing at. The query runs, returns...
Read guideWritten to be followed, not skimmed
Every guide here is a procedure someone actually ran, with the steps, the time it takes and the places it goes wrong.
Numbered steps, not prose
Each guide breaks into steps with their own anchor, so you can send a colleague straight to step four. Those anchors are minted once and never move.
Levels you can read without colour
Beginner to Expert, shown as filled pips as well as a label, so the level survives a screenshot, a colourblind reader and a printout.
An honest time estimate
The minutes on each card are the sum of its steps, not a reading speed. Guides without an estimate sort last rather than first — an unknown length is not a short one.
Freshness that means something
“Updated” moves only when a person changed the content — never when someone read it. Sorting evergreen guides any other way ranks whatever was most recently opened.
Filters that admit when they are empty
Every count is measured with your other filters applied, so the number beside an option is what you will actually get. Options that cannot narrow anything are not offered.
Prerequisites up front
What you need before you start, and what you will be able to do at the end, are stated before the first step — not discovered halfway through.
