Prompt quick start

Learn to send requests to the LLM using Agent Actions Prompt.

Prompt is a Sanity Agent Action that lets you make large language model (LLM) requests without bringing in external AI tooling. You can run prompts from anywhere you can execute code, such as Sanity Functions, custom components, webhook listeners, CI/CD pipelines, migration scripts, and more.

In this guide, you'll first use Prompt to make a request to the LLM. You'll use @sanity/client to run the Prompt (you can also make requests using the HTTP API directly).

Prerequisites:

• @sanity/client v7.4.0 or higher and an environment to run client requests.
• API Version vX is required for any requests to the Agent Actions APIs.
• Optional: In Node.js v23.6 and above, you can run the TypeScript examples below without additional servers or build processes. Alternatively, you can use earlier versions with an experimental flag. Converting the examples to JavaScript is okay too.
• An API or personal token to make authenticated requests.

Import and configure @sanity/client with the projectId, dataset, API token, and an apiVersion of vX.

import { createClient } from "@sanity/client";

export const client = createClient({
    projectId: '<project-id>',
    dataset: '<datset-name>', // such as 'production'
    apiVersion: 'vX',
    token: '<editor-token>'
})

If you're already using the client elsewhere in an application, you can reuse its base configuration. If you need to adjust the token and/or API version, use the withConfig method to create a new client based on your existing one. For example:

// ...
const promptClient = client.withConfig({
  token: '<your-token>',
})

Prompts don't edit or create documents. They return text, or json, so you can use it however you please.

const response = await client.agent.action.prompt({
  // write an instruction
  instruction: `Give me some ideas for a blog post 
    about using AI with structured content.`
});

console.log(response)

This prompt returns a text response that you can use. The examples on this page log the response to the console.

You can further customize the instruction by passing in parameters. For example, if you want use a document as background information for the prompt, you use the document parameter type.

const response = await client.agent.action.prompt({
  // write an instruction
  instruction: `Give me some ideas for a blog post 
    about using AI with structured content. Use the following as context for the ideas: $background`,
  instructionParams: {
    background: {
      type: 'document',
      documentId: '<target-document-id>'
    }
  }
});
console.log(response)

You can learn more about parameters and the available types in the creating instructions guide.

Prompt will return a text response by default, but you can also tell it to return JSON. To do so, you must set the format to "json" and explicitly include the world "JSON" or "json" in the instruction. It also helps to provide an example shape in the instruction.

const response = await client.agent.action.prompt({
  // write an instruction
  instruction: `Give me some ideas for a blog post 
    about using AI with structured content. Respond in JSON with the following format: { "ideas": ['idea one', 'idea two', 'etc'] }`,
  format: 'json'
});
console.log(response)

You can tune the variance of responses by adjusting the temperature of the request.

const response = await client.agent.action.prompt({
  // write an instruction
  instruction: `Give me some ideas for a blog post 
    about using AI with structured content.`,
  temperature: '0.8' // Set between 0 and 1, inclusively. Default: 0.3
});
console.log(response)

Higher values result in more variety of responses, while lower values result in more predictable results when given the same instruction.

To learn more about what you can do with Translate, explore the other guides and resources available for Agent Actions.