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How to Improve AI Apps with Error Analysis
No more “prompt-and-pray”
AI is powering a new generation of smarter, more intuitive software products. However, if you’ve ever built on top of an LLM, you know that excitement can quickly turn into frustration when the model makes mistakes for no clear reason. In this article, I’ll discuss error analysis, a technique that takes the guesswork out of AI engineering and helps you improve an AI app at the points of greatest leverage.
LLMs are probabilistic. That means they can feel more like rolling dice than running a computer program.
While this randomness gives LLMs their flexibility, it also presents a key challenge: how do you build reliable software on top of an unpredictable model?
One answer to this question is error analysis. Here I’ll discuss what that is, how it works, and walk through a concrete example of doing it.
What is Error Analysis?
Error analysis is the process of identifying the most significant failures of a machine learning (ML)…
