An LLM Evaluation Framework for AI Systems Performance

Automated performance metrics for AI systems with Microsoft.Extensions.AI.Evaluation

One of the challenges of AI systems development is ensuring that your system performs well not just when it is initially released, but as it grows and is deployed to the world. While AI prototyping projects are fun and exciting, eventually systems need to make it to the real-world and evolve over time.

These evolutions can come in the following forms:

• Changing the system prompt to try to improve performance or resolve issues
• Replacing the text completion or embedding model used by your system
• Adding new tools for AI systems to call in function-calling scenarios. This is particularly relevant when working with tooling like Semantic Kernel or Model Context Protocol (MCP)
• Changing the data that is accessible to models for Retrieval Augmentation Generation. This often comes naturally over time as new data is added.

Regardless of the cause of change, organizations need repeatable and effective means for evaluating how their conversational AI systems respond to common contexts. This is where Microsoft.Extensions.AI.Evaluation comes in as an open-source library that…