Librarian Hotline: The Difference Between Asking AI and Thinking With AI
AI can make us better thinkers or replace the thought process entirely. The difference lives in how we show up to the conversation.
The reference interview is a core practice in library science. When a patron approaches the desk and says “I need information about climate change,” a librarian doesn’t pull a stack of books and call it done. They ask questions: What are you trying to understand? What do you already know? What will you do with what you find? The conversation is part of the work, because the quality of what we find depends on the precision with which we’ve named what we’re looking for.
AI breaks this contract. A model trained on enormous amounts of human language is extraordinarily fluent at sounding authoritative regardless of how well we’ve framed our question. It will answer when the question is underspecified, when context is missing, when what we need isn’t quite what we asked for. Unlike a librarian, it rarely tells us we’ve come in with the wrong question. It will just answer the one we gave it, confidently and completely, and leave the gap between what we asked and what we needed entirely invisible.
The breakdown happens in what we do before and after the prompt, not in the prompt itself.
Reader Question
“I’ve been using AI at work for about six months. I ask questions, use what comes back, and move on. But something has started to bother me: sometimes I can’t fully explain decisions I’ve made with AI’s help, or I’ll use an AI-generated summary and find out later it missed something important. A coworker told me I’m just ‘prompting wrong,’ but that doesn’t feel like the whole answer — I follow prompt engineering best practices. What’s going on?”
— Dev P.
A note before we begin: Thank you for being here, whether you’re a free or paid subscriber. This post covers what cognitive offloading actually costs us, why engagement with AI changes everything about what we get from it, and what thinking partnership looks like in practice (available to everyone), then goes deeper into the frameworks that make the difference between AI that sharpens us and AI that does the work for us (for paid subscribers).
Librarian Answer
Your coworker is addressing a symptom. Prompting technique matters, but what you’re describing runs deeper: the cognitive work of evaluation, interrogation, and synthesis got handed off to the model, and it wasn’t equipped to do it. AI can produce fluent, confident-sounding output without understanding your context, your stakes, or what “right” means for your specific situation. That judgment belongs to you, and it doesn’t happen automatically just because you’re using the tool.
When We Outsource the Thinking
There’s a version of AI use that looks like research but functions more like delegation. We bring a question, receive an answer, and move on. The output arrives formatted and fluent, which triggers the same cognitive response we’d have to a well-organized report: it seems finished and authoritative, so we treat it that way.
What disappears in that exchange is cognitive friction, and with it goes the understanding that friction was building. Cognitive friction is the effortful feeling of working through a problem rather than accepting the first answer that arrives. It’s what happens when we read a source and push back on a claim, when we try to explain something and realize we can’t quite do it yet, or when two conflicting accounts force us to decide which one to trust. That discomfort is also how we build the capacity to hold information well, to know when to trust a conclusion and when to keep pulling the thread
A 2011 study published in Science found that when people know they can look something up, they're less likely to retain it - the brain treats external systems as a kind of storage offload. The effect may be more pronounced with AI, which doesn't only retrieve information but synthesizes and presents conclusions. We're not even doing the organizational work of sorting through results anymore; the model is. And when we consistently let it, we risk not developing the judgment that comes from doing that work ourselves.
Dev’s situation reflects this directly. When we can’t explain a decision we made with AI’s help, the reasoning may never have been built in the first place: a conclusion arrived, got used, and the thinking that would let us defend or interrogate that conclusion never took place. What looks like a productivity gain in the short term carries a real risk, and over months and years of consistent practice, the pattern recognition and critical judgment we rely on can erode in ways we don’t notice until we need them.
The gap between “I have an answer” and “I understand this” widens every time we skip the friction, and no amount of better prompting narrows that distance. Output quality matters, but the deeper problem is the thinking we bypass in order to get there faster.
Why Think With AI at All
The case against passive AI use is also the case for something better.
A model trained on the breadth of human knowledge can hold a vast amount of context simultaneously and draw on it in response to what we specifically bring. When we bring something real — a problem we’re stuck on, a position we’re not sure we believe, a decision we can’t seem to make — the model has something to work with, and what comes back is material for thinking rather than a substitute for it.
Used this way, AI can surface a counterargument we hadn’t considered, push back on an assumption we didn’t know we were making, or expose a connection across two bodies of knowledge that might otherwise be difficult to find. A researcher who asks a model to challenge their hypothesis before they finalize their analysis catches problems earlier. A manager who uses AI to pressure-test a proposal before presenting it walks into the room with stronger reasoning. A writer who asks for the weakest point in their argument and then rewrites that section themselves produces better work than either step would produce alone.
In none of those cases did the model do the thinking. It created conditions for better thinking to happen.
This is also what accumulates over time in the opposite direction from passive use. Instead of eroding judgment by outsourcing it, active engagement builds it, because every time we interrogate an output, test it against what we know, and decide what to do with it, we’re doing the cognitive work that makes us sharper at the next decision. Passive use and active engagement compound in opposite directions. The question is which one we’re practicing.
Thinking with AI is less about caution and more about return on investment. When we stay in the room as active participants, the work tends to get better — and so does our capacity to do it.
The Confidence Problem
AI models don’t know when they’re wrong in a way that flags itself clearly. Incorrect information arrives in the same confident register as correct information. The model isn’t hedging based on its actual certainty: it has no epistemic state to draw on. It’s producing the most statistically plausible continuation of our prompt, which is a fundamentally different operation than verifying a fact or reasoning through a problem.
When a doctor says “I’m not sure, let me check,” that uncertainty is meaningful information. It tells us something real about the state of their knowledge. An AI doesn’t have that mechanism. It can be instructed to say “I’m not certain,” but that phrase doesn’t correspond to any internal state of knowing or not knowing. It’s just another output pattern, produced in some cases and not others, without consistent correlation to whether the information is accurate.
The missing summary detail Dev mentions is one of the most common and least visible ways this plays out. When an AI summarizes a document, it’s selecting for what sounds like a coherent summary, which isn’t the same as selecting for what we need to know. A model summarizing a research report tends to foreground the headline findings, the ones that read like conclusions, and compress or drop the methodological detail that would change how we interpret them — not because it made a judgment call, but because that detail doesn’t pattern-match to “key finding.”
The same dynamic appears with contested or time-sensitive questions. Ask an AI about an ongoing legal case, a company’s current leadership, or a scientific question still being actively debated, and it will often produce an answer that sounds settled, because the model is trained to produce answers, and answers sound confident by nature. The confidence is structural, not earned.
Healthy skepticism toward AI output isn’t excessive caution. It’s an appropriate response to a tool that can be wrong without any indication of error.
The Reference Interview, Applied
A practiced reference librarian doesn’t answer the question as asked. They answer the question behind the question — the one the patron actually came in with, which is often different from the one they put into words.
The reference interview works because the librarian and patron are thinking together toward something neither could reach alone. Asking AI for an answer is a transaction: we know what we want, we’re just retrieving it. A thinking partnership is something different — we arrive with something unresolved and use the conversation to work through it, with the model as a collaborator in the process rather than a dispenser of conclusions.
What the shift looks like in practice:
Asking AI: “What are the main causes of employee burnout?”
Thinking with AI: “Three people on my team seem fine, two seem checked out, and the only thing that changed two months ago was switching to asynchronous communication. I keep assuming it’s the structure, but what else should I be considering before I draw that conclusion?”
