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

# Quickstart

> Go from blank workspace to a deployed fine‑tuned assistant and live Chat API.

## 1. Sign in and create an assistant

1. Open the Promptify app at `https://app.promptify.one`.
2. **Sign up / log in** from the auth screen.
3. Navigate to **Onboarding** in the sidebar.
4. Walk through the onboarding chat and describe:
   * who your assistant is for,
   * what it should and shouldn’t do,
   * tone, style, and formatting preferences.

When you’re done, Promptify creates an `assistants` row in Supabase with your **description**, **system prompt**, and **base model**.

## 2. Start a fine‑tuning job

1. Go to the **Dashboard**.
2. Click **Start Training** on the Fine‑Tuning Job card.
3. Behind the scenes, Promptify will:
   * generate a JSONL training dataset via OpenAI (`real-fine-tune.ts`),
   * upload it to OpenAI Files,
   * start a fine‑tuning job,
   * poll status via `/api/fine-tune/status`,
   * update your assistant to point to the fine‑tuned model when ready.

Stay on the Dashboard to watch log output and progress.

## 3. Talk to your assistant

Once training has finished (job status `SUCCEEDED`):

1. Open the **Assistant** page.
2. Send a few prompts and verify:
   * responses follow your spec and tone,
   * edge cases are handled as expected.

This page calls your authenticated internal API (`/api/assistant/message`) using the same assistant and model that the public API will use.

## 4. Use the Chat API

Now you can call the Chat API directly from your own code:

* Endpoint: `POST /api/v1/chat`
* Auth: `Authorization: Bearer <access_token>`
* Body:

```json theme={null}
{
  "message": "How do I reset my password?",
  "conversationId": "optional-conversation-id"
}
```

See the dedicated [Chat API reference](/docs/api-reference/chat) for full request/response details and example snippets.