What is Tonic Validate?

The Tonic Validate application and SDK (tonic_validate) allow you to measure how well your RAG LLM system performs.

What is a RAG system?

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.

Using Validate to evaluate development and production RAG systems

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 Validate comes in.

As you develop your RAG system, you can use a Validate development project to run tests to determine how the system performs against a benchmark set of questions. You can then see whether the quality of the answers improves for each run.

After you release your RAG system, you can configure it to send the questions, answers, and context to a Validate production monitoring project that tracks the quality of the responses over time in your production systems.

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 your RAG LLM system performance, so that you can deploy it with confidence.