---
schema: 1
type: term
title: >-
  **Prompt context** — the examples, vision, and guidance that shape a better AI
  answer
description: >-
  **Prompt context** is the surrounding information you give an AI with a prompt
  — the context, vision, and examples that steer it toward a better answer. It
  includes who the audience is ('people who love the internet'), the shape or
  structure you want ('split into smaller groups'), and illustrative examples
  ('like their favorite social media app').
canonical: >-
  https://liquidgold.cc/channel/everyinc/explore/term/prompt-context-the-examples-vision-and-guidance-tha-c573282ef478
markdown: >-
  https://liquidgold.cc/channel/everyinc/explore/term/prompt-context-the-examples-vision-and-guidance-tha-c573282ef478.md
llms: null
parent: 'https://liquidgold.cc/channel/everyinc/llms.txt'
channel: everyinc
updated: '2026-10-23T22:12:35.346Z'
language: null
children: []
---


# \*\*Prompt context\*\* — the examples, vision, and guidance that shape a better AI answer

Canonical: https://liquidgold.cc/channel/everyinc/explore/term/prompt-context-the-examples-vision-and-guidance-tha-c573282ef478

Parent: [Channel index](https://liquidgold.cc/channel/everyinc/llms.txt)

## Definition

**Prompt context** is the surrounding information you give an AI with a prompt — the context, vision, and examples that steer it toward a better answer. It includes who the audience is ('people who love the internet'), the shape or structure you want ('split into smaller groups'), and illustrative examples ('like their favorite social media app').

In this channel's material, **prompt context** appears in Steph Smith's prompting lesson: the way to change what an AI produces is to give it **context, a vision, and examples** — and the output changes in response.- **Context** tells the model who or what it is working with — e.g., 'people who love the internet'.- **Vision** describes the shape you want — e.g., 'split into smaller groups'.- **Examples** show the model the kind of result you mean — e.g., 'like their favorite social media app'.Each piece narrows the space of possible answers so the model aims where you want it.In Steph Smith's workflow, a single meetup icebreaker prompt improves when she layers in the audience ('people who love the internet'), a structure ('split into smaller groups'), and a reference point ('like their favorite social media app'). The broader habit in the channel's material is the same: **shape the answer you want before asking again**. Use the redo button before adding more, tell it explicitly 'this is the kind of answer I want', and treat wrong-but-close answers as valuable — they help you refine what you actually want. Prompt context is one part of that refinement loop, alongside knowing the mistakes the model is likely to make and how to check and edit.- **Prompt context = context + vision + examples** given to the AI before or inside a prompt.- Supplying even one of these ('like their favorite social media app') measurably changes the output.- Pair it with the redo button and explicit feedback ('this is the kind of answer I want') to refine results fast.- Wrong-but-close answers still help: they clarify what you actually want.