AI is only as good as your data.

But collecting robust datasets for training and testing can a major challenge.

We’re really excited to share that you can now generate high-quality RAG Q&A datasets using your own documents on Athina.

You can then use this generated data for evaluation, testing prompts and models, running experiments or fine-tuning. All from within Athina.

Click to see a 30-second demo.

How does synthetic data generation work?

We partnered with Fiddlecube to leverage their advanced data generation techniques.

A lot things are happening under the hood to generate high quality data:

• The source data is run through a data generation pipeline, which uses large language models to generate rows with diversity.
• The dataset is then measured for quality, and rigorously filtered, cleaned and de-duped to meet the described criteria.
• Ultimately, the output rows will be RAG Question-Answer style rows with a query, context, and response.

We can generate synthetic data of various types, including: