Is your AI product actually working? How to develop the right metric system

In my first stint as a machine learning (ML) product manager, a simple question inspired passionate debates across functions and leaders: How do we know if this product is actually working? The product in question that I managed catered to both internal and external customers. The model enabled internal teams to identify the top issues faced by our customers so that they could prioritize the right set of experiences to fix customer issues. With such a complex web of interdependencies among internal and external customers, choosing the right metrics to capture the impact of the product was critical to steer it towards success.

Not tracking whether your product is working well is like landing a plane without any instructions from air traffic control. There is absolutely no way that you can make informed decisions for your customer without knowing what is going right or wrong. Additionally, if you do not actively define the metrics, your team will identify their own back-up metrics. The risk of having multiple flavors of an ‘accuracy’ or ‘quality’ metric is that everyone will develop their own version, leading to a scenario where you might not all be working toward the same outcome.

For example, when I reviewed my annual goal and the underlying metric with our engineering team, the immediate feedback was: “But this is a business metric, we already track precision and recall.” 

First, identify what you want to know about your AI product

Once you do get down to the task of defining the metrics for your product — where to begin? In my experience, the complexity of operating an ML product with multiple customers translates to defining metrics for the model, too. What do I use to measure whether a model is working well? Measuring the outcome of internal teams to prioritize launches based on our models would not be quick enough; measuring whether the customer adopted solutions recommended by our model could risk us drawing conclusions from a very broad adoption metric (what if the customer didn’t adopt the solution because they just wanted to reach a support agent?).

Fast-forward to the era of large language models (LLMs) — where we don’t just have a single output from an ML model, we have text answers, images and music as outputs, too. The dimensions of the product that require metrics now rapidly increases — formats, customers, type … the list goes on.

Across all my products, when I try to come up with metrics, my first step is to distill what I want to know about its impact on customers into a few key questions. Identifying the right set of questions makes it easier to identify the right set of metrics. Here are a few examples:

Did the customer get an output? → metric for coverage

How long did it take for the product to provide an output? → metric for latency

Did the user like the output? → metrics for customer feedback, customer adoption and retention

Once you identify your key questions, the next step is to identify a set of sub-questions for ‘input’ and ‘output’ signals. Output metrics are lagging indicators where you can measure an event that has already happened. Input metrics and leading indicators can be used to identify trends or predict outcomes. See below for ways to add the right sub-questions for lagging and leading indicators to the questions above. Not all questions need to have leading/lagging indicators.

Did the customer get an output? → coverage

How long did it take for the product to provide an output? → latency

Did the user like the output? → customer feedback, customer adoption and retention

Did the user indicate that the output is right/wrong? (output)

Was the output good/fair? (input)

The third and final step is to identify the method to gather metrics. Most metrics are gathered at-scale by new instrumentation via data engineering. However, in some instances (like question 3 above) especially for ML based products, you have the option of manual or automated evaluations that assess the model outputs. While it’s always best to develop automated evaluations, starting with manual evaluations for “was the output good/fair” and creating a rubric for the definitions of good, fair and not good will help you lay the groundwork for a rigorous and tested automated evaluation process, too.

Example use cases: AI search, listing descriptions

The above framework can be applied to any ML-based product to identify the list of primary metrics for your product. Let’s take search as an example.

How about a product to generate descriptions for a listing (whether it’s a menu item in Doordash or a product listing on Amazon)?

The approach outlined above is extensible to multiple ML-based products. I hope this framework helps you define the right set of metrics for your ML model.

Sharanya Rao is a group product manager at Intuit.

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