---
title: Universal prompts | Adaption
description: Apply a single instruction to every row of your dataset when your data doesn't have a per-row prompt column.
---

Sometimes every row of your dataset should be evaluated or processed against **the same instruction**—but your data doesn’t contain a prompt column. Instead of engineering per-row prompts upstream, you can define the instruction once as a **universal prompt** and let Adaptive Data apply it uniformly at scale.

This is a web-app workflow on the **columns** step of the adaptation wizard. The walkthrough below picks up after you’ve uploaded your data and reached column selection. For the broader concepts behind the columns step, see [Column selection](/guides/column-selection/index.md).

## Step 1 — Check “I don’t have prompt”

On the columns step, locate the **Prompt column** card and check **I don’t have prompt** beneath it.

![The columns step of the wizard with the Prompt column card and its "I don't have prompt" checkbox highlighted, alongside the Context column and Completion column cards.](/universal-prompts/dont-have-prompt.png)

The prompt card transforms to expose two alternatives: **Let adaption create prompt** (a per-row generated prompt for each completion) and **Write universal prompt** (a single instruction applied to every row). Universal prompts use the second path.

## Step 2 — Click “Write universal prompt” and enter your instruction

Click **Write universal prompt** to reveal the textarea, then type your instruction into the **Enter prompt for all entries…** field.

![The expanded prompt card highlighting the "Write universal prompt" link and a textarea with placeholder "Enter prompt for all entries..." underneath, with the earlier "Let adaption create prompt" toggle visible above.](/universal-prompts/universal-prompt.png)

Your universal prompt should be a **complete, self-contained instruction**: the role, the task, and the expected output format.

A useful shape:

> \[Task description]. Respond with \[exact output format].

The tighter you specify the output format, the more consistent your completions will be—and the cleaner the resulting training signal. For classification tasks, constrain the model to a fixed vocabulary. For generation tasks, specify length, tone, and structure.

## Next steps

After column selection you’ll move on to the wizard’s later steps. Two are particularly worth knowing about for universal-prompt runs:

- [Reasoning traces](/guides/reasoning-traces/index.md) — produce a structured chain-of-thought alongside each completion, useful when the reasoning matters as much as the label.
- [Brand controls](/guides/safety-and-length-constraints/index.md) — encode length, safety, and freeform brand voice that should apply to every completion the platform generates.