Experiments
An experiment on Confident AI is a contained way to benchmark LLM applications. You can create an experiment on Confident AI and define evaluation metrics for it to evaluate and test your LLM application's performance at scale. Running an experiment produces a test run, which is the evaluation results of the tests cases that your LLM application was evaluated on.
You can evaluate test cases produced by your LLM application directly on Confident AI by simply sending over test cases via deepeval with fields such as actual_output and retrieval_context populated by your LLM application. All compute and LLMs required for evaluation are provided by Confident AI.
Creating An Experiment
You can easily create an experiment on the Evaluation page by providing your experiment with a unique name and a set of metrics to start with. In this RAG use case example, we have named our experiment "RAG Experiment" and have chosen the 'Answer Relevancy' and 'Contextual Relevancy' metric as a starting point.
You can then edit the metric configurations (such as threshold), add additional metrics, or even change the experiment name on another individual experiment page once you have created an experiment.
Running Your First Evaluation On Confident AI
We DON'T recommend doing this until you're 100% happy with the evaluations you run locally as explain in the previous section. Running evaluations on Confident AI brings no additional benefits apart from the fact that you can trigger it on the platform without going through code. This benefits non-technical team members that need no-code workflows, but also means you lose the capability to fully customize your metrics.
Once you've created an experiment, all you need to do is setup an LLM connection endpoint. Once this is done, simply press the Evaluate button on the Evaluations page, select your experiment, and press evaluate!