How AI Studio Helps You Produce Quality Data

Did you know that 80% of AI project time is spent on data preparation, not on training or fine-tuning models? If you’ve worked on LLMs, you’ve likely felt this yourself. Hours (or days) are spent cleaning logs, filtering out noise, removing sensitive details and attempting to expand datasets. This is actually long before you get to the exciting part of running experiments. And since your model’s performance depends directly on the quality of your data, this step cannot be overlooked. 

In this blog, we’ll show you how AI Studio helps you produce quality data with ease.

Why Quality Data Matters More Than You Think

Your model’s performance is only as good as the data behind it. High-performing LLMs rely on:

• Clean datasets that are free of noise, duplicates, or irrelevant content
• Well-structured data organised in a way that the model can easily learn from
• Context-rich examples providing enough information for accurate reasoning

Try feeding a model inconsistent or incomplete data and watch the results be unpredictable, like:

• Hallucinations or fabricated outputs
• Irrelevant or off-topic responses
• Biases creeping into model behaviour
• Gaps in reasoning or knowledge

Data from logs, user interactions or previous model outputs often needs extensive cleaning and organisation before it’s ready for training or fine-tuning. You may find that issues like missing context, duplicated examples or unbalanced datasets can reduce accuracy. Even minor inconsistencies can turn into outputs that fail to meet expectations, forcing costly retraining cycles.

And the stakes could be high in domain-specific applications. For example, a customer support model trained on incomplete logs may provide misleading guidance, while legal or medical models trained on biased data can produce potentially dangerous errors.

How AI Studio Helps You Produce Quality Data

High-quality datasets ensure that your fine-tuned models deliver outputs users can trust. And that’s exactly what AI Studio does. Our full-stack Gen AI platform helps you spend less time cleaning the data and more time fine-tuning, experimenting and deploying market-ready products. 

Let’s explore how:

Data Preparation Made Easy

Before you can fine-tune or train, you need to get your logs and datasets in order. AI Studio provides a drag-and-drop UI and API support that makes uploading files effortless.

• Upload your training data in JSONL format.
• Group and organise interactions into datasets using tags, so you can later find and reuse them.

For example, if you are training a domain-specific customer support model, tag logs as “Billing Queries,” “Technical Issues” or “Cancellations.” This makes it easy to create targeted datasets for fine-tuning.

Scaling with Data Synthesis

When model outputs cannot be directly used for training other models, you must opt for data synthesis. As data synthesis can help you:

• Repurpose outputs from a previous model for training
• Generate variations of your existing data while preserving its original characteristics

How to Synthesise Data in AI Studio

AI Studio makes it easy to generate high-quality synthetic training data directly from our UI. Here’s how you can do it step by step:

1. Visit the Logs and Datasets Page

Open the Logs & Datasets page and switch to the Data tab to see all your available datasets.

2. Select Logs to Synthesise

By default, all logs in the chosen dataset are included. To focus on specific data, you can apply filters such as:

• Tags: For example, Billing, Technical Support or Feedback
• Models: Select outputs from specific models you want to synthesise

3. Start Synthesis

Click the “Synthesize Logs” button and confirm the action. AI Studio will generate synthetic variations of your selected logs while maintaining their original characteristics.

4. Review Results

Once the process is complete:

• You’ll receive a success notification
• The logs table allows you to toggle between Original and Synthetic versions for easy comparison.

Get Started with AI Studio

If you’re ready to explore AI Studio but don’t have a dataset on hand, don’t worry you can start experimenting immediately with our sample dataset (click here to download the dataset). 

You can choose from a range of popular models to fine-tune, including:

• Mistral Small 24B Instruct
• Llama 3.3 70B Instruct
• Llama 3.1 8B Instruct

Even better, you can try fine-tuning for less than $1* to test your ideas without any heavy upfront investment. You can start small, experiment and see how quickly you can turn raw or synthetic data into high-quality and fine-tuned models. AI Studio gives you all the tools to prepare, synthesise and scale your datasets, even if you’re just getting started.

*Finetuning for under $1 applies only to the example dataset in the tutorial for Llama 3.1 8B and Mistral Small 24B using default hyperparameters. Actual charges may vary based on workload or dataset size.

Build Market-Ready AI with AI Studio

FAQs

What is AI Studio?

AI Studio is a platform that simplifies dataset preparation and synthesis for LLM training and fine-tuning.

Why is data quality important for LLMs?

High-quality data ensures accurate, reliable outputs, reduces bias and improves model reasoning and performance.

How can I upload datasets to AI Studio?

You can upload JSONL files via drag-and-drop or API and organise them with tags and filters.

What is data synthesis and why is it needed?

Data synthesis generates new examples from existing logs, useful when outputs can’t be directly reused for training.

Can I try AI Studio without my own dataset?

Yes, you can experiment with a sample dataset (click here to download the dataset) and fine-tune popular Llama and Mistral models.

How much does fine-tuning cost on AI Studio?

Fine-tuning can be under $1 for the example datasets using default hyperparameters; actual costs depend on dataset size.