> For the complete documentation index, see [llms.txt](https://docs.uptiq.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.uptiq.ai/platform-resources/skill-library/ai-skills/prompt.md).

# Prompt

**Prompt** is the workflow's direct channel to an LLM. It sends instructions, a query, and optionally documents to a model and returns the response — the way you bring generation and reasoning into an automated process.

{% hint style="info" %}
**Reference layer pending.** This page documents the skill's purpose and configuration as used in the builder. Its formal configuration schema, output schema, and error codes are not yet documented — see the [Skill Library](/platform-resources/skill-library.md) overview.
{% endhint %}

## Worked example: summarizing customer feedback

A workflow collects feedback from survey responses and support tickets. The team needs a concise summary of each — key themes, overall sentiment — rather than a pile of raw text.

**The problem.** Reading and summarizing large volumes of feedback by hand is slow and inconsistent between whoever does it.

**The approach.** Send each piece of feedback to an LLM and ask for a summary.

**1. Choose the model.** Pick from the **Model** dropdown — Gemini-2.5-Flash, gpt-4, and a continuously growing list.

<figure><img src="/files/hX8SLLnvpAU9T6sZcQEf" alt=""><figcaption><p>Selecting the model</p></figcaption></figure>

**2. Write the instruction.** **Instruction to LLM** (formerly "System Prompt") sets the model's role for this task:

> You are a helpful assistant specialized in summarizing customer feedback. Your task is to provide a concise, neutral summary of the user's input, highlighting key points and overall sentiment.

<figure><img src="/files/gIY1sSMkwvBGCUSxNCVU" alt=""><figcaption><p>Giving the model its role and task</p></figcaption></figure>

**3. Set the query.** Point **User's Query** at the text to summarize — `$input.customerFeedbackText`.

**4. Attach documents (optional).** When the response needs extra context — a product manual, a policy — attach them by **Document ID(s)** (comma-separated) or **Base 64 Content**.

<figure><img src="/files/sWptkAW2TdO1J4hl0xYO" alt=""><figcaption></figcaption></figure>

## Advanced configuration

Defaults are usually right. Change these when you've measured a reason to:

| Setting                          | What it does                                                                                         |
| -------------------------------- | ---------------------------------------------------------------------------------------------------- |
| **Temperature**                  | Controls creativity/randomness in the response. Lower is more predictable — better for factual work. |
| **Number of Conversation Turns** | How many previous messages the model should consider. Default `3`.                                   |
| **Response Format**              | Plain text by default. Set to JSON when a later step parses the output.                              |

## Output

* **`content`** — the model's generated response.
* **`systemPrompt`** — the Instruction to LLM that was sent, returned for traceability.
* **`error`** — descriptive message; `null` on success. **Particularly useful when `responseFormat` is JSON and the model failed to produce valid JSON.**
* **`statusCode`** — `200` success · `400` bad request · `500` service-side failure.

{% hint style="warning" %}
Setting **Response Format** to JSON asks the model for valid JSON; it does not guarantee it. Check `error` before a downstream step tries to parse `content`, or the failure surfaces somewhere less obvious.
{% endhint %}

***

To add this skill to an agent, see [Adding a Skill to the Agent](/agent-builder/build/adding-a-skill-to-the-agent.md). To ground responses in your own documents rather than the model's training, see [RAG Query](/platform-resources/skill-library/data-skills-rag/rag-query.md).


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.uptiq.ai/platform-resources/skill-library/ai-skills/prompt.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.