Understand the Limits of What an AI Model Can Reliably Do

The people who get the most value from AI models are the ones who understand where the model is likely to be wrong. That means knowing which tasks it handles reliably, which ones produce plausible but wrong output, and what the failure modes look like. This covers all three honestly, including what to watch for in serious work, and a practical test for when you are not sure whether to trust an output.

I want to understand what large language models like you are actually reliable for, what they are unreliable for, and why. Cover: the types of task where models perform well (and what makes a task well-suited), the types where they produce plausible but wrong output (and why), the specific failure modes I should watch for when using one for serious work, and one test I can apply when I am unsure whether to trust a specific output. Be honest about your own limitations; do not claim more certainty than the evidence supports.