20 posts tagged with "How-to"

To have AI edit a Word document with tracked changes, the AI has to change the .docx itself instead of handing back rewritten text. In MindMax you @ the file, describe the change once, review the edits in groups, and the ones you accept are written into the same file as Word tracked changes — styles, numbering and headers untouched.

That sounds like a small detail. It is the whole difference between an edit you can send to a client and an afternoon spent rebuilding a document that used to be fine.

To review a Word document with AI and send back real comments, the AI has to anchor each comment to the words it is about, and you have to be the one who decides what is sent. In MindMax you say what the review is for, adopt the drafted comments you agree with in your own words, and export them into a copy of the .docx as ordinary Word comments.

The point of a review is not the opinion. It is an opinion the author can act on, on the sentence it is about, in the file they already work in.

A decision matrix is a table with your options down the side and your criteria across the top; you score every option on every criterion, optionally weight the criteria, and compare. Its value is less the final total than the discipline: every option is judged on the same ruler, and the criterion you were quietly ignoring becomes a column you have to fill in.

The last post in Thinking models in practice, a short series on classic methods and how to run them on your own material.

Six thinking hats is a method for looking at one question from six angles, one at a time: white for facts, red for feelings and instinct, black for risks, yellow for value, green for new ideas and blue for managing the process and deciding the next move. Its point is separation — not arguing optimism against caution in the same breath.

Part of Thinking models in practice, a short series on classic methods and how to run them on your own material.

To write a thesis chapter with AI without losing the argument, settle the thesis and outline yourself before any prose is drafted, write from your own sources rather than the model's memory, keep a running record of what the draft has already argued, defined and promised, and review every AI change as a marked edit. Also: check your institution's rules on AI use before you start, not after.

The usual approach — ask a chat assistant for one section at a time — produces sections that are each fine and a chapter that does not hold together.

To answer an RFP, tender or security questionnaire with AI, give it the questionnaire as the structure and your current policies as the only source, require a quote from a named document under every answer, and check every answer that states a number, date, certification or promise. Treat any answer without a quote as a question for a colleague, not a draft to send.

Drafting is the easy part now. The expensive mistakes are elsewhere.

SCAMPER is a checklist of seven prompts for improving an existing idea, product or process: Substitute, Combine, Adapt, Modify, Put to another use, Eliminate, Reverse. You run the idea through each prompt in turn and write down what it suggests. It works because it forces you past the two or three moves you would make anyway.

Part of Thinking models in practice, a short series on classic methods and how to run them on your own material.

To turn voice notes into Markdown without breaking your flow, use a global shortcut that opens a capture box over whatever app you are in, read the transcript before it saves, and save it to today's note as plain text. Only then — and only if you want — let AI tidy how you said it, never what you said.

The usual voice-memo workflow fails at the second step. You record, the audio sits in an app you never open, and the transcript, when you finally get one, is a wall of "um, so, the thing is".

First principles thinking means breaking a problem down to the things you are confident are true — physical limits, real costs, actual requirements — and reasoning up from those, instead of starting from how the problem is usually solved. It is slow and effortful, so it is worth using only where convention may be wrong and the stakes justify the work.

Part of Thinking models in practice, a short series on classic methods and how to run them on your own material.

An AI skill is a plain-text file — usually SKILL.md — with a name and a one-line description of when it applies at the top, and the method below: the steps, the checks, the shape of the output. To write one that gets used, describe the situation it applies to rather than its contents, keep the method concrete enough to be followed literally, and fix it the first time it lets you down.

The third time you type the same instruction into a chat box — "decisions first, each with an owner and a date" — that sentence has become a skill. It just has not been saved yet.