> ## Documentation Index
> Fetch the complete documentation index at: https://docs.attensira.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Prompts

> Add the questions people actually ask assistants, and manage the prompt slots your plan gives you.

## What is a prompt?

A prompt is a question a person actually types into an assistant, written the way they would type it. "What's the best expense tool for a 20-person agency?" is a prompt. "expense tool agency" is a keyword, and no one asks it. Attensira runs each prompt against the model surfaces you track and reads the answer that comes back.

The distinction matters because the answer is prose, not a ranked list. A model replies with a paragraph or two naming a handful of products, sometimes with links attached. What Attensira measures from that answer is whether your brand was named (a **mention**), whether the model attached a URL on your domain (a **citation**), and which domains it drew on (**sources**). Keyword-shaped input produces a thin, unnatural answer that tells you nothing about how buyers are actually being advised.

In code and in the API, a prompt is a `keyword_id`. The name is historical; the object is a prompt.

<Tip>
  Write prompts in the words your buyers use, not your own category language. If your customers say "invoice chasing" and your site says "accounts receivable automation," track both and compare.
</Tip>

## How do I add prompts?

<Steps>
  <Step title="Open the prompts view in your workspace">
    Every prompt belongs to one workspace. A workspace is one brand and one set of prompts, competitors, and model surfaces.
  </Step>

  <Step title="Enter one question per line">
    Phrase each as a complete question. Avoid your brand name in the prompt itself unless you specifically want to measure branded questions — an unbranded prompt is the honest test of whether a model reaches for you unprompted.
  </Step>

  <Step title="Save">
    Each new prompt consumes one slot. If you are at your limit, the save is rejected until you remove something.
  </Step>
</Steps>

You can also add prompts programmatically with the `add_prompts` MCP tool. That tool costs credits. See [MCP overview](/mcp/overview).

```json theme={null}
{
  "prompts": [
    "What is the best expense tool for a 20-person agency?",
    "How do small teams handle receipt capture without a finance hire?"
  ]
}
```

New prompts do not backfill. A prompt starts producing data from its first scheduled run onward, so the history you see for it begins the day you added it.

## When does a prompt actually run?

An hourly ticker picks up scheduled work, but each prompt × model × country combination runs **at most once per workspace-local calendar day**. Adding a prompt at 09:00 and again pressing save at 11:00 does not get you two readings. The daily ceiling is the unit of measurement.

How many readings that day produces depends on sampling depth: **n=1 on Starter**, **n=3 on Growth and Business**, issued concurrently. Deeper sampling is why the same prompt on Growth gives a steadier rate than on Starter — one reading can only ever be 0% or 100%.

Runs that fail are stored but excluded from `n`. A failed run is not a zero; it is an absence, and the metrics exclude it rather than counting it against you.

## How do prompt slots work?

A prompt slot is a **stock, not a flow**. A slot is occupied for as long as a prompt is tracked, and it returns to your pool the moment you remove that prompt. You are not buying a monthly allowance of prompts you can burn through — you are buying how many prompts can be under observation at once.

| Plan     | Price / month | Prompt slots | Sampling depth | Retention |
| -------- | ------------- | ------------ | -------------- | --------- |
| Starter  | \$129         | 50           | n=1            | 3 months  |
| Growth   | \$349         | 150          | n=3            | 12 months |
| Business | \$699         | 350          | n=3            | Unlimited |

Yearly billing is charged at ten months' price. There is no Pro plan.

So a Starter workspace that adds 50 prompts, removes 10 in March and adds 10 different ones in April is still within its 50 slots. Nothing accumulates and nothing expires.

<Warning>
  Removing a prompt frees its slot immediately, but the readings already collected stay subject to your plan's retention window. Removing a prompt does not delete its history, and re-adding the same text later starts a fresh prompt rather than resuming the old one.
</Warning>

## How do I remove a prompt?

Delete it from the prompts view, or call the `remove_prompt` MCP tool. The slot is returned as soon as the removal is accepted, so you can add a replacement in the same sitting.

`remove_prompt` is marked as a destructive tool in the MCP schema. An agent holding your key can remove tracked prompts, so treat the key as a workspace-level credential — see [Authentication](/account/api-keys).

Remove prompts when the question stopped mattering, when two prompts turned out to be the same question in different words, or when a prompt is producing answers that never mention anyone in your category. Rotate slots deliberately rather than hoarding them: a tracked prompt you never read is a slot doing no work.

## Reading the numbers on a prompt

Every rate is reported as a value plus the count it came from, `{value, n}`. A `value` of `null` means **not measured** — no successful run exists for that window. A `value` of `0` with `n` above zero is a real, measured zero: the model was asked and did not name you.

Changes over time carry `{value, real}`. Attensira runs a two-proportion z-test at 95% confidence, and a change that does not clear the noise floor is returned as `{real: false, value: null}` — "no change we can prove," which is not the same as no change.

Next: group related prompts with [Topics](/tracking/topics), and choose which surfaces answer them in [Models](/tracking/models).