What is Tonic Validate?

Retrieval augmented generation (RAG) allows you to augment a large language model (LLM) with additional data that is not in the LLM's original training set. The additional data usually takes the form of text from documents such as HTML, MarkDown, Word, or Notion.

The LLM can then use that data in its responses to user queries. The responses can also include references for the additional content.

But how do you know how well your RAG system works? How good are its responses? How relevant is the additional context? And how does the quality of the responses change when you change the available context data?

That is where Tonic Validate comes in. To help you to develop your RAG LLMs, you use the Validate platform to evaluate your RAG LLM responses.

Validate includes:

• Metrics to measure the performance of each component in your RAG system

• Visualizations to compare performance across time as the system changes

Validate provides insight into how well your RAG LLM system performs, so that you can deploy it with confidence.