It feels like everywhere you turn these days, generative AI is the topic of conversation. From crafting marketing copy to writing code, its potential seems boundless. But how do we move beyond the buzz and truly understand if these powerful tools are delivering on their promises? That's where robust evaluation comes in, and platforms like Microsoft Foundry are stepping up to help.
Think of it like this: you wouldn't launch a new product without rigorous testing, right? The same principle applies to generative AI. To really get a handle on how well a model or application is performing, especially with a substantial amount of data, you need to put it through its paces. This isn't just about a quick check; it's about a deep dive, measuring performance using both established mathematical metrics and AI-assisted ones. This thorough assessment is crucial for understanding both what these systems can do and where their limitations lie.
Microsoft Foundry offers a dedicated space for this kind of evaluation. It's a portal designed to give you the tools to assess the performance and safety of your generative AI models. Within Foundry, you can log, view, and really dig into the detailed metrics that tell the story of your AI's capabilities. The process starts with creating an evaluation run, which can be done directly from the Foundry portal's 'Evaluate' page, or even from the playground pages for models or agents.
When you initiate an evaluation, the first key decision is the 'evaluation target.' Are you looking to assess a standalone 'Model,' which means evaluating the output from a specific model and your custom prompt? Or are you testing an 'Agent,' which involves evaluating its responses based on a prompt? Alternatively, you might already have a dataset containing outputs from your model or agent, in which case you'd select 'Dataset' as your target.
If you're evaluating a model or agent, you'll need a dataset to feed it. Foundry makes this flexible. You can upload your own data, provided it's in CSV or JSON Lines (JSONL) format. For those situations where you might not have readily available data, or need to test specific scenarios, Foundry offers synthetic dataset generation. This feature allows you to create data based on a prompt describing the kind of information you need, and you can even upload existing files to guide the generation process, ensuring the synthetic data is relevant to your task. It's worth noting that synthetic data generation isn't available everywhere, so it's good to check regional support.
Once your target and dataset are set, it's time to configure the testing criteria. Microsoft has curated a set of metrics designed to provide a comprehensive view. These fall into a few key categories: 'AI quality (AI assisted)' metrics, which use another AI model as a judge to assess overall quality and coherence; 'AI quality (NLP)' metrics, which are mathematical and evaluate content quality, often requiring ground truth data but not necessarily a separate AI judge; and 'Risk and safety metrics,' focused on identifying potential issues and ensuring content safety. And if these built-in options don't quite fit the bill, you have the flexibility to create your own custom metrics.
This structured approach to evaluation, moving beyond anecdotal evidence to concrete, measurable results, is what will truly help us understand and harness the power of generative AI. It's about building trust and ensuring these technologies are not just innovative, but also reliable and safe.