Evaluating LLM-powered applications with Azure AI Studio

An implementation with Prompt Flow

In the rapidly expanding realm of Generative AI, the rise Large Language Models is transforming the way we interact with technology. However, unlike the more traditional machine learning models, the novelty of these generative AI systems has outpaced the development of standardized evaluation metrics to assess their performance. Traditional metrics, typically used in machine learning, are insufficient to capture the nuanced complexities of LLM outputs.

For example, let’s consider a binary classification algorithm, trained to classify reviews into “positive” or “negative” categories. We could easily evaluate this model using a labeled test training set and simply counting the number of correct predictions over the total number of records. This ratio is called Accuracy and it is a core evaluation metric in classification algorithms. However, if you think about a standard conversation with models like ChatGPT, you can imagine that accuracy or similar metrics are not suited to evaluate the model’s response.

This lead to the necessity of establishing new, innovative techniques.

In this article, we will delve into mechanism of evaluating LLM-powered applications with AI-assisted metrics, providing an implementation in…