Andrew Ng

Rigorous Evals and Error Analysis Accelerate AI Agent Development

Readers responded with both surprise and agreement last week when I wrote that the single biggest predictor of how rapidly a team makes progress building an AI agent lay in their ability to drive a disciplined process for evals (measuring the system’s performance) and error analysis (identifying the causes of errors). It’s tempting to shortcut these processes and to quickly attempt fixes to mistakes rather than slowing down to identify the root causes. But evals and error analysis can lead to much faster progress. In this first of a two-part letter, I’ll share some best practices for finding and addressing issues in agentic systems. Even though error analysis has long been an important part of building supervised learning systems, it is still underappreciated compared to, say, using the latest and buzziest tools. Identifying the root causes of particular kinds of errors might seem “boring,” but it pays off! If you are not yet persuaded that error analysis is important, permit me to point out: - To master a composition on a musical instrument, you don’t only play the same piece from start to end. Instead, you identify where you’re stumbling and practice those parts more. - To be healthy, you don’t just build your diet around the latest nutrition fads. You also ask your doctor about your bloodwork to see if anything is amiss. (I did this last month and am happy to report I’m in good health! 😃) - To improve your sports team’s performance, you don’t just practice trick shots. Instead, you review game films to spot gaps and then address them. To improve your agentic AI system, don’t just stack up the latest buzzy techniques that just went viral on social media (though I find it fun to experiment with buzzy AI techniques as much as the next person!). Instead, use error analysis to figure out where it’s falling short, and focus on that. Before analyzing errors, we first have to decide what is an error. So the first step is to put in evals. I’ll focus on that for the remainder of this letter and discuss error analysis next week. If you are using supervised learning to train a binary classifier, the number of ways the algorithm could make a mistake is limited. It could output 0 instead of 1, or vice versa. There is also a handful of standard metrics like accuracy, precision, recall, F1, ROC, etc. that apply to many problems. So as long as you know the test distribution, evals are relatively straightforward, and much of the work of error analysis lies in identifying what types of input an algorithm fails on, which also leads to data-centric AI techniques for acquiring more data to augment the algorithm in areas where it’s weak. With generative AI, a lot of intuitions from evals and error analysis of supervised learning carry over — history doesn’t repeat itself, but it rhymes. [Truncated due to length limit. Full text: https://lnkd.in/gjqv6VeA ]