AI Agent: Bad evaluations waste your time? Here's how to avoid it.
·2 min read·Intermediate
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Ever tried to make an AI do something, and the results were just... weird? Often, it's not the AI's fault, but how we test it.
In 30 seconds
01Evaluating AI agents is complex, often leading to errors that slow down development.
02An expert identified 7 common evaluation mistakes, costing weeks of work.
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What this means for you
For anyone working with AI, this is a valuable lesson: the problem isn't always the model; often, it's the test. It means less time wasted doubting the AI and more time getting it to actually work, avoiding unnecessary frustration.
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·1 min·1·Intermediate
03These issues are solvable with simple adjustments, sometimes a single line of code.
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AI Agents: Why are evaluations a nightmare?
Getting AI agents to do complex stuff is already messy, but evaluating them is the real dark art. An engineer, Debashish Ghosal, wasted weeks chasing test results that just didn't add up, only to find the fault was in his evaluation methods, not the agent itself. It's like testing a cake with a broken thermometer: you'll never know if it's baked.
Many think you just ask the AI if it did well, but that's a bit like asking a kid if they did their homework. The truth is, evaluation systems are full of traps. Ghosal documented these "traps" on dev.to, identifying seven main mistakes that slowed his projects down for quite a while. Debashish Ghosal published a detailed analysis on dev.to about 7 common evaluation mistakes that cost him several weeks.
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What are the most common mistakes and how to fix them?
The most common "oversights" involve how we phrase evaluation questions, the data we feed the AI for judgment, and even the environment where we run tests. Ghosal showed that the solution is often trivial, like changing a single sentence or parameter, but finding it is the real puzzle.
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A classic mistake? Asking the AI to "summarize the text" when maybe we wanted a summary "in three bullet points." If you're not specific, the AI will do what it thinks you want, and your test will say it failed, even if it did a great job from its perspective. It's the classic communication problem, but with an algorithm.
Other times, the issue lies in the "ground truth" data we use to compare the AI's responses. If your comparison data is inaccurate or incomplete, even the agent's best answer will look wrong. Ghosal solved many of these headaches with changes that were sometimes literally a single line of code, turning weeks of frustration into a sigh of relief. Who would've thought such a tiny problem could stall everything for weeks on end? Ghosal's proposed solutions for AI agent evaluation errors include precise rephrasing of instructions and accurate verification of ground truth data, often resolved with minimal changes.
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