Not 100%. You have to understand that AI tools are programmed to try their best to answer your question or perform your task, but there are limitations. How recent is the data in the model? How much does the model leverage what’s currently online? Does it use reasoning to question itself on the answer before giving it? Here are some things to consider when using AI in terms of its reliability.
Treat every AI answer as draft notes from an enthusiastic intern—helpful, but unvetted. Large language models are trained to sound fluent, not to guarantee factual accuracy. Do a quick gut-check. If a claim feels surprising, pause before accepting it. Demand citations—and open them. Missing or broken citations are an instant yellow flag; a citation that doesn’t actually support the point is red. Check the sources and look for other ones. Probe the AI’s uncertainty. “List reasons your answer might be wrong” or “Rate your confidence 0 to 100 %.” When you do catch an error, feed the correction back to the AI and see how the answer changes. It turns fact-checking into an iterative conversation. Use a ‘reasoning’ model that simulates thought processes better and will question its own conclusions.
Four Analogies to Remember
AI as an improv actor — great at staying “in character,” but will riff if it doesn’t know its lines.
AI as an over-confident GPS — almost always on the right road, occasionally wants you to drive into a lake.
AI as a blender — it purées everything in its training data; your job is to strain out the pulp.
AI as a first-draft factory — speedy and tireless, but your editing is where accuracy is forged.
Karl Plesz
Your Productivity Guru
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