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

# API Quickstart

> Start improving your LLM in under 5 minutes using the API

## Get an access key

[https://auth.getplum.ai](https://auth.getplum.ai)

Sign up here to get a Plum AI API key.

## Call the Plum AI - API

### Recommended to use Postman

[Instead of running `curl` commands, you can also use the API via the Postman interface.](https://www.postman.com/research-astronomer-38444972/workspace/plum-api-beta-endpoints/collection/39138842-02681bf5-2542-4a19-8f3f-36058f6683d9?action=share\&creator=39138842)

We recommend **Postman** over `curl`.

### Step 1: Upload your data

All you need to get started is:

1. The system prompt you’ve been using
2. User prompts (inputs) you’ve sent to the LLM
3. Outputs that the LLM has returned

```bash theme={null}
curl -H "Authorization: YOUR_KEY_HERE" -d '{"data":[{"input":"THE FIRST USER PROMPT HERE", "output":"THE FIRST LLM RESPONSE HERE"},
{"input":"ANOTHER USER PROMPT HERE", "output":"THE SECOND LLM RESPONSE HERE"}], "system_prompt":"YOUR SYSTEM PROMPT HERE"}' beta.getplum.ai/v1/data/seed
```

You will get a dataset ID in the response. Keep it somewhere safe because you’ll need it later.

### Step 2: Generate metric definitions based on your system prompt

The ability to evaluate your model comes from well-defined metrics. Plum creates criteria based on your business use case, which it then uses to score the model.

```bash theme={null}
curl -H "Authorization: YOUR_KEY_HERE" -d '{"system_prompt":"YOUR SYSTEM PROMPT GOES HERE"}' beta.getplum.ai/v1/questions
```

Keep the metrics ID in the response somewhere safe. You’ll need it in the next step.

### Step 3: Generate evaluation results based on your uploaded data

In this step, Plum returns a score for each of the criteria defined in the previous step. These scores are out of a maximum of **5**.

These evaluation results will be used in the next step. Plum’s goal is to boost the lowest of these scores.

```bash theme={null}
curl -H "Authorization: YOUR_KEY_HERE" -d '{"prompt":"YOUR SYSTEM PROMPT GOES HERE", 
"seed_data_id":"YOUR SEED DATA ID FROM STEP 1 GOES HERE", 
"metrics_id":"YOUR METRICS ID FROM STEP 2 GOES HERE"}' beta.getplum.ai/v1/evaluate
```

### Step 4: Generate synthetic data based on your uploaded seed data and evaluation results

This step generates synthetic inputs and outputs, each pair being a variation of one of the seed pairs you uploaded in Step 1.

Adjust the `multiple` to adjust the size of the dataset you would like to generate. For each example in your seed dataset, Plum will generate `multiple` synthetic examples similar to it.

You will need at least 50-100 examples, so set the `multiple` accordingly.

```bash theme={null}
curl -H "Authorization: YOUR_KEY_HERE" -d '{
"multiple":2, 
"seed_data_id":"YOUR SEED DATA ID GOES HERE", 
"eval_results_id":"YOUR EVAL RESULTS ID FROM STEP 3 GOES HERE"}' beta.getplum.ai/v1/augment
```

### Step 5: Download the synthetic data in a format that OpenAI’s fine-tuning UI accepts

OpenAI provides the ability to fine-tune and customize their latest models. In order to do this, you will use the synthetic data that Plum generated in the previous step.

```bash theme={null}
curl -H "Authorization: YOUR_KEY_HERE" beta.getplum.ai/v1/data/synthetic_openai/1729625334491 > train.jsonl
```

### Step 6: Upload the synthetic data to a major LLM provider like OpenAI’s fine-tuning API

Once you have a `train.jsonl` from Step 5, you could optionally create another file, `validation.jsonl`, using real heldout data that you haven’t used in the seed dataset.

1. Go to the OpenAI fine-tuning page: [https://platform.openai.com/finetune](https://platform.openai.com/finetune)
2. Click “Create”.
3. Upload new training data.
4. Optional: Select “Upload new” validation data. Drag your validation `.jsonl` file to upload.

After around 15 minutes, the fine-tuning run completes, and OpenAI will provide a customized model ID that you can start using.

Congratulations! You’ve completed one round of fine-tuning using Plum AI.

Unlock your data flywheel: generate a new set of data using your fine-tuned model, create synthetic data using Plum AI, and start another round of fine-tuning.