> For the complete documentation index, see [llms.txt](https://docs.hypertune.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.hypertune.com/ai-configuration/guide.md).
# Guide
This guide builds on the SDK quickstart and shows you how to model, manage, and experiment on your AI configuration using Hypertune.
You'll learn how to:
* Create custom object types to model your AI configuration
* Create flags that use those custom object types
* Access those flags to retrieve AI configuration in your code
* Update and target your AI configuration
* Run experiments on your AI configuration
## Prerequisites
[Set up Hypertune](/getting-started/set-up-hypertune.md)
## Create custom object types to model your AI configuration
Go to the **Schema** view in the dashboard. Click the **+** button in the top-right of the sidebar. Select **Object**, enter a name, and click **Create**.
By default, the new object type has no fields.
Click **+ Add** to add a new field. Enter a name, set its type, and click **Create**.
Repeat for each field you want to add. You can switch to the code view to make this easier. Then click **Save**.
```graphql
type EmailAssistantAIConfig {
model: String!
system: String!
prompt(email: String!, tone: String!): String!
maxOutputTokens: Int!
temperature: Float!
presencePenalty: Float!
frequencyPenalty: Float!
maxRetries: Int!
}
```
Note how the example above has `email` and `tone` arguments on the `prompt` flag. They are passed when calling the `prompt` flag in your code, and can be referenced as variables when writing the prompt in the Hypertune dashboard, enabling you to create a prompt template.
## Create flags for your AI configuration
Go to the **Flags** view in the dashboard. Click the **+** button in the top-right of the sidebar and select **Flag**.
Enter a name, set its type to the one you created earlier, and click **Create**.
Enter the initial configuration, then click **Save**.
## Access flags to retrieve AI configuration
Regenerate the client:
{% tabs %}
{% tab title="npm" %}
```bash
npx hypertune
```
{% endtab %}
{% tab title="yarn" %}
```bash
yarn hypertune
```
{% endtab %}
{% tab title="pnpm" %}
```bash
pnpm hypertune
```
{% endtab %}
{% endtabs %}
Then use the generated methods to access your flags:
{% code title="app/api/completion/route.ts" %}
```typescript
import { waitUntil } from '@vercel/functions'
import { generateText } from 'ai'
import getHypertune from '@/lib/getHypertune'
export async function POST(req: Request) {
const hypertune = await getHypertune({ isRouteHandler: true })
const aiConfig = hypertune.emailAssistantAIConfig()
const { email, tone }: { email: string; tone: string } =
await req.json()
const { text } = await generateText({
model: aiConfig.model({ fallback: 'openai/gpt-4.1' }),
system: aiConfig.system({
fallback: `You are a professional assistant that drafts clear, polite, and concise email replies for a busy executive.`,
}),
prompt: aiConfig.prompt({
args: { email, tone },
fallback: `Write a reply to the following email:\n\n${email}\n\nThe tone should be ${tone} and the response should address all points mentioned.`,
}),
maxOutputTokens: aiConfig.maxOutputTokens({ fallback: 400 }),
temperature: aiConfig.temperature({ fallback: 0.5 }),
presencePenalty: aiConfig.presencePenalty({ fallback: 0.1 }),
frequencyPenalty: aiConfig.frequencyPenalty({
fallback: 0.3,
}),
maxRetries: aiConfig.maxRetries({ fallback: 5 }),
})
waitUntil(hypertune.flushLogs())
return Response.json({ text })
}
```
{% endcode %}
## Update your AI configuration
Go to the **Flags** view in the dashboard, and select the flag with your AI configuration from the left sidebar.
Make your changes, then open the **Diff** view to review them. Click **Save**.
## Target your AI configuration
Go to the **Flags** view in the dashboard, and select the flag with your AI configuration from the left sidebar. Click the arrow (**>**) to view each subfield in the sidebar, and select the field you want to add a targeting rule to.
Click **+ Rule**, set your condition, provide alternate configuration for that condition, and click **Save**.
## Experiment on your AI configuration
Go to the **Flags** view in the dashboard, and select the flag with your AI configuration from the left sidebar. Click the arrow (**>**) to view each subfield in the sidebar, and select the field you want to add a targeting rule to.
Click **+ Experiment**. In the dropdown, select **New experiment**.
Enter a name for your experiment and click **Create**.
Click **Insert** and update the **Test** variant of your content.
Review your changes in the **Diff** view and click **Save**.
Once you've [analyzed your experiment results](/experimentation/guide.md#analyze-experiment-results) and decided on a winning variant, go to the **Flags** view and select your flag. Click the options button (⋯) next to the variant you want to ship and select **Ship variant**, then click **Save**.
## Next steps
* Run a [multivariate test](/concepts/multivariate-tests.md) to find the best combination of model choice, prompts, and settings to optimize key metrics like cost, latency, or user satisfaction.
* Set up an [AI loop](/concepts/ai-loops.md) to automatically optimize AI configuration for each unique user to optimize key metrics.
* Extend your schema to support more complex AI configuration, e.g. prompt chains.