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

# Enrich Your Dataset to Improve Fine-Tuning Results

> Learn how to increase your dataset size using Prem Studio's data enrichment tools for better model training and generalization.

## 🧠 Why This Matters

A small dataset can limit a model’s ability to generalize and perform well on unseen data — especially in domain-specific tasks.

With Prem Studio, you can enrich your dataset using synthetic data generation strategies. This allows you to:

* 📈 Expand your dataset size with low human effort
* 🧠 Introduce diversity into training examples
* 🚀 Improve model performance and generalization during fine-tuning
* 🧪 Prototype quickly, even with minimal real data

In this guide, we’ll show you how to enrich an existing dataset with synthetic data — using a customer support chatbot use case.

## 💼 Use Case: Fine-Tuning a Customer Support Chatbot

Imagine you’re building a domain-specific chatbot to answer product-related queries for an e-commerce company. You’ve collected only 50 QA pairs from past support tickets — not enough for robust fine-tuning.

Instead of manually creating more data, you can enrich your dataset using synthetic generation in Prem Studio.

This guide shows you how.

## ⚙️ Step-by-Step: Enrich Your Dataset with Synthetic Data

<Steps>
  <Step title="Select Your Dataset and Perform a 50/50 Split">
    <img src="https://static.premai.io/prem-saas-docs/guides/datasets/select-dataset-and-split.gif" alt="GIF of opening a dataset and creating a validation split" />

    From the sidebar, go to **Datasets** and open your 50-row dataset.

    Split it **50/50**: 25 datapoints for training and 25 for validation.
    This ensures your evaluation is based on a meaningful validation size, despite the small dataset.

    Only examples in the **Training** and **Uncategorized** buckets will be used during enrichment.

    <Warning>
      It’s not mandatory, but we highly recommend splitting your dataset into training and validation sets before running enrichment. This helps avoid data leakage and ensures more reliable evaluations.
      See our [Dataset Best Practices Guide](/guides/datasets/dataset-best-practices) for more.
    </Warning>
  </Step>

  <Step title="Launch the Enrichment Workflow">
    <img src="https://static.premai.io/prem-saas-docs/guides/datasets/click-enrich.gif" alt="GIF of clicking enrich dataset button" />

    In the top right corner, click **Enrich Dataset**. Then choose **Seed data enhancement**.
    You’ll be enriching the 25 training datapoints to boost generalizability.
  </Step>

  <Step title="Define Enrichment Settings and (Optional) Instructions">
    <img src="https://static.premai.io/prem-saas-docs/guides/datasets/custom-instructions-enrich.gif" alt="GIF of setting enrichment arguments and instructions" />

    Set the following:

    * **New pairs to generate:** `500`
    * **Creativity:** `0.1` (lower creativity = safer, more consistent results)

    Use a higher value if your use case requires more creative outputs (e.g. roleplaying).

    * **Instructions (optional):**

    ```
    Keep answers short and helpful. Focus on product-related questions, shipping issues, return policies, and discount inquiries.
    Avoid repetitive or overly technical questions.
    ```
  </Step>

  <Step title="Review and Approve Synthetic Examples">
    Click **Generate**. Review the 500 synthetic examples.
    Approve the ones you'd like to keep.
  </Step>

  <Step title="Add Synthetic Datapoint to Training Bucket">
    <img src="https://static.premai.io/prem-saas-docs/guides/datasets/synthetic-to-training-bucket.gif" alt="GIF of moving synthetic datapoints to training bucket" />

    In case the data quality matches your expectations, you can continue by adding all the datapoints to the **Training** bucket.

    <Warning>
      This happens automatically when using the **Autosplit** functionality if "Allow synthetic data in Validation" is **not selected** — even if you apply a split like 80/20 or 70/30.
    </Warning>
  </Step>

  <Step title="(Optional) Further Enrich Using Textual Documents">
    <img src="https://static.premai.io/prem-saas-docs/guides/datasets/enrich-from-files.gif" alt="GIF of moving synthetic datapoints to training bucket" />

    You can optionally upload additional textual sources — such as **PDFs, TXT files, or HTML pages** — to provide contextual grounding for enrichment.

    These documents (e.g. product manuals, help center articles, policy pages) are used alongside your **seed datapoints** to generate more realistic and context-aware synthetic QA pairs.

    <Info>
      When documents are provided, the enrichment engine combines both seed examples and content from your uploaded files to create new, high-quality datapoints that better reflect your domain language and topics.
    </Info>

    This step is useful if you have internal documentation or unstructured content that the model can learn from.
  </Step>
</Steps>

Once enrichment is complete, your dataset will contain both original and synthetic entries — ready to be used for model fine-tuning.

## 📊 Example Before and After Enrichment

| Type      | Question                                    | Answer                                                                     |
| --------- | ------------------------------------------- | -------------------------------------------------------------------------- |
| Original  | How can I track my order?                   | You can track it using the link in your confirmation email.                |
| Synthetic | Where do I check the status of my shipment? | Use the tracking link in the confirmation email we sent you.               |
| Synthetic | Can I know where my package is?             | Yes, the tracking link in your confirmation email shows real-time updates. |

## 📊 Dataset Size: Before vs After

Started with 50 original examples.

* Split: 25 training / 25 validation
* After enrichment: +500 synthetic datapoints → 550 total (525 in training + 25 in validation)

## 📦 What’s Next?

With your enriched dataset, you can now:

* Fine-tune a model with higher data diversity ([Fine-Tuning Guide](../../finetuning/get-started))
* Evaluate model generalization with [agentic evaluation](../../evaluations/get-started)

## 💡 Pro Tips

* Always enrich **after** splitting to avoid data leakage.
* Use instructions to control output tone, complexity, topic, or QA structure.
* Review synthetic data for consistency — quality > quantity.
* Avoid over-relying on synthetic examples for evaluation.