Prompting and reasoning

A reusable request with blanks to fill in, so the same job is asked for the same way every time instead of retyped.

A shared collection of tested prompts a team draws on, which is how prompting stops being one person's private skill.

Breaking a job into several requests where each one's output feeds the next, usually more reliable than asking for everything at once.

Telling a model what part to play, such as a cautious reviewer, to set the tone and standard of what comes back.

How reliably a model does what it was actually told, especially when the request has several conditions that must all hold.

A request organised into labelled sections rather than one paragraph, which makes long instructions easier for a model to follow.

Having a model check and revise its own answer before returning it, which catches some mistakes and costs an extra step.

A model working out the steps before doing any of them, which is what separates an agent from a single answer.

The visible record of steps a model took to reach an answer, useful for spotting where it went wrong and not always complete.

Deciding what a model should have in front of it for a task, now often more decisive than the wording of the request itself.

A setting controlling how widely a model draws when choosing its next word, the usual companion to a temperature setting.

Hidden instructions planted in content a model reads, aiming to make it ignore its real task, and the central security problem of AI tools.

A model revealing the private instructions it was set up with, exposing internal wording that was assumed to stay unseen.

The shape you ask an answer to come back in, such as a table or a list, and the simplest way to make results usable.