Good morning. No “How I used AI this week” today; I want the whole hour for this, because on Friday several of you asked, some of you pretty directly, for concrete technique. Things you wouldn’t have thought of. So that’s today: how to get a good answer out of a chatbot, and how to know when you’ve got one. On Friday I sat in on the small groups, and I want to start with what you said, because it shaped this talk. No names; you know who you are. \[Click\] One of you, after hearing a trick for making answers shorter, said that little things like that, which we wouldn’t have thought of, would be really helpful to learn. I heard that as a hint that I should be teaching more concrete stuff. Fair. \[Click\] One of you said you’d been using it on rotations more as a crutch than a tutor, and someone else said they related to that. \[Click\] One of you said that after Thursday, you trust it a lot more than you should. \[Click\] And one of you, who uses it every day, said you haven’t unlocked it the way you want to. \[Click\] Two more that were already the answer. One of you asked ChatGPT itself how to use it as a tutor, and it came up with a prompt that had it act as a Socratic tutor: it asks you questions, you explain in your own words, it grades you. You found you knew less than you thought, which is the point. We’ll come back to asking it to write the prompt. \[Click\] And one of you said your habit is: this is my current differential, what am I missing? You called it a safeguard, to make sure you’re doing the work first. That’s the best single pattern I know, and it’s in Part 4. \[Click\] And the usual ways an answer disappoints: it made something up, it was too generic, too long, confidently wrong, it agreed with you too easily, or, honestly, you weren’t sure what you wanted. Each maps onto part of today: made up and confidently wrong go to Check; too generic to Clarity; agreed too easily to Conversation; and not sure what you wanted to Goal, which is where we start, and the most fixable. So today is concrete moves, each with the evidence behind it where there is some, and a short set of rules. Several of the rules came from you; one group came up with them on their own on Friday, and they’re good. LIVE: Run sheet for the 60-minute slot: 45 minutes of core, 5 of buffer, 10 for questions. 0 to 4: opening. 4 to 13: Part 1 (confidence question, about 30 seconds; frontier; when it’s safest; find your own frontier; tired or rushed; the gap is in the conversation). 13 to 19: Part 2 (done, with the “which could you check?” question). 19 to 29: Part 3 (growing prompt about 30 seconds a frame, six parts, template, let it write the prompt, flip cards). 29 to 40: Part 4 (sycophancy, “are you sure?”, the exercise about 6 minutes, follow-ups, AI review). 40 to 44: Part 5 (toolbox, keeping your chats, the “try this week” question). 44 to 45: the card and Wednesday. OPTIONAL, skip if behind, in this order: “Examples, and the thinking dial”; “Things that do matter”; “Keep what you made”; “The jagged frontier” talk-over shortened to one sentence. LIVE: Before class: open the deck once with ?presenter=YOUR-KEY so the live questions open. Turn on Zoom live captions and paste the deck link into the chat. The exercise case was pre-run on 2026-10-18 (see its notes); re-run it once in the morning in case the model changed.

Here’s the shape of the hour. Five steps. Goal: before you type anything, what does done look like? Clarity: what does the model need to know to get there? Conversation: the first answer is a draft; push back, don’t just accept it. Check: the one claim the plan depends on, against a real source. And Stop: either you’re done, or you start over. This isn’t my invention. It’s roughly what the companies building AI agents had to write down to make their agents work, and we’ll see that in a few minutes. It works for people too.

First: where is it safe to use a chatbot at all? The honest answer is that it depends on the task, and the boundary isn’t where you’d guess. So the first skill is knowing which side of it you’re on, and the way you learn that is by trying things where being wrong is cheap.

Before the frontier, a quick check. How confident are you that you can tell, for a given task, whether a chatbot will help, and whether it’s safe to use? Not at all to very. LIVE: Open the question, about 30 seconds, read the spread aloud. If most say “fairly” or “very”, say the next slide is why that confidence is hard to earn: the boundary is invisible. If most say “a little”, say that’s the honest answer, and that’s what this part is for.

A study of 758 management consultants at BCG, Dell’Acqua and colleagues, now published in Organization Science, examined where AI helps and where it hurts. They called the boundary the jagged frontier. The line draws itself here; that’s the boundary of what the model does well. It’s jagged, and it’s invisible. \[Click\] Inside the line, the consultants who used AI did 12% more tasks, 25% faster, at over 40% higher quality. These are the kinds of things that are usually inside for us: practice questions, turning your notes into a table, summarizing a guideline you’ve pasted in, rewriting something for a patient. \[Click\] Outside the line, the people using AI were 19% less likely to get the right answer than people who didn’t use it at all. And it doesn’t feel different when you’re outside. It’s just as fluent and just as confident. For us: a citation from memory, a dose for a patient on dialysis, what changed in a guideline last month, noticing what’s missing from a case. These placements are illustrative. Where your tasks actually fall is something you learn by checking, which is part of what this course is for. LIVE: The slide uses the published version’s wording, “19% less likely”. The 2023 working paper says 19 percentage points; if asked, that’s the difference.

So when is it safest to use? Here’s how I sort it. Two questions. How bad is it if the answer’s wrong? And how easy is it for me to check? \[Click\] Low stakes, easy to check: go ahead. Quiz me. Turn my notes into a table. Draft an email I’ll read before I send. Explain a concept I’m going to read about anyway. If it’s wrong, I’ll notice, and nobody’s hurt. \[Click\] Low stakes, hard to check: use it for ideas, not answers. Brainstorming a research question, a study plan, different ways to say something hard. You’re not trusting it; you’re using it to think. \[Click\] High stakes, easy to check: use it, then check the one claim the plan depends on. A drug interaction, then DailyMed. A risk score, then MDCalc. A summary of a guideline you pasted in, against the guideline. \[Click\] High stakes, hard to check: don’t, or give it the source first. A dose from memory. A diagnosis you can’t verify. A citation you aren’t going to open. This is exactly where the frontier hurts: the answer looks the same as everywhere else. This isn’t from a study; it’s a way of sorting. The book’s centaur-and-cyborg chapter asks the same question a different way: who’s harmed if it’s wrong?

So if the frontier is invisible, how do you find it? You experiment, in the corner where being wrong costs nothing. Ask it something you already know cold, a topic you’ve just studied, and watch where it slips. Redo a question-bank item you’ve already done and compare its reasoning with the explanation. One caution: check your question bank’s license or terms first; many don’t allow copying their items into other tools. Rewriting the question in your own words, or using questions you wrote, avoids the problem. Ask the same question in two chats, or in ChatGPT and another tool; where they disagree, you’re probably near the edge. And keep a short list, even a note on your phone, of what it’s been good and bad at for the things you actually do. \[Click\] Use it for everything where a wrong answer costs nothing. That’s how you learn where it helps, and it’s how you get faster at the rest of what we do today. The same experiments are a bad idea in the top row of the last slide: there, you check first, or you don’t use it. This is my advice, not a finding. The frontier study tells you the boundary is there and hard to see; it doesn’t tell you how to map it.

And the question of when you use it isn’t only about the task. It’s about you. There’s a name for this: automation bias, the tendency to over-rely on automation. It was named long before chatbots, with autopilots and clinical decision support. A systematic review of 74 studies found it gets worse with workload, task complexity, and time pressure. What helps: training, and feeling personally accountable for the result. Which is you, signing the note. \[Click\] And we know what fatigue does by itself. In a randomized study in ICUs, interns working 24-hour shifts made about 36% more serious medical errors than on a schedule without them, and 5.6 times as many serious diagnostic errors. That’s without any AI at all. \[Click\] Put those together. Post-call, behind on notes, at 2 a.m.: that’s exactly when you most want to paste the answer in, and exactly when you’re least likely to check it. So my rule, which I mentioned I don’t break: when I’m tired or rushed, I either stay in the safe corner from the last slide, or I don’t use it. Write it down tonight, check it in the morning.

Why bother learning to use it well? Because having the tool isn’t enough. In last week’s physician trial, doctors were 74% without GPT-4 and 76% with it. In a big trial this year, chatbots alone named the right condition 95% of the time, and members of the public using those same chatbots got under 35%, no better than a search engine. And the good news. The same Stanford group that ran the physician trial went back and designed the interaction. The clinician and the AI each made their own assessment first, then the AI wrote a comparison highlighting where they agreed and disagreed. Seventy clinicians, randomized. With usual resources: 75%. \[Click\] With the designed workflow: 82 to 85%, depending on who went first. \[Click\] AI alone: 90%. The workflow got the clinicians most of the way there, and neither workflow was significantly different from AI alone. \[Click\] So how you work with it changes the result, and it’s learnable. Notice what they built in: you think first, then compare. That comes back. That’s the rest of today.

Part 2. This is the part most prompting advice skips, and I think it matters most. Before you type anything: what is your goal? And part of a good goal is knowing what done looks like.

There’s a big literature on this from organizational psychology: goal-setting theory, Locke and Latham, 35 years of studies summarized in 2002. The central finding is simple. People given a specific, hard goal do better than people told to “do your best.” “Do your best” feels encouraging, but it gives you nothing to aim at and no way to know when you’ve arrived. “Help me understand heart failure” is a do-your-best goal. It’ll give you a pleasant overview, and you’ll have no idea whether it worked. \[Click\] Compare: a one-page table, HFrEF versus HFpEF, three drugs each with how they work, done when I can answer five board-style questions without looking. Now the model knows what to make, and you know when to stop. \[Click\] What works for people works for the model, for the same reason: neither one can read your mind.

Here’s the connection to agentic AI, which you’ve probably heard about: AI that takes a series of steps on its own. To make agents work, the companies building them had to write down, explicitly, what any good worker needs. A goal: what done looks like. Then act, check against the goal, and ask, am I done? If not, go again. If so, stop. The dot going around is an agent working. The hard part, it turns out, isn’t the acting. It’s the goal and the stopping. An agent with a vague goal never stops, or stops too early and says it’s done. \[Click\] Anthropic’s own guide says: think of the model as a brilliant but new employee who lacks context on how you work. And their golden rule: show your prompt to a colleague with no context. If they’d be confused, the model will be too. \[Click\] OpenAI’s guide for GPT-5 literally writes the stop condition into the prompt: only end your turn when you’re sure the problem is solved. If the companies had to write this down for their own models, so should you. And honestly, it’s the same thing your attendings want from you on a presentation.

So, before you start, four lines. What will I have at the end: a table, a draft, a list of three sources. Who it’s for, which changes everything about the answer. What must be in it. And how I’ll know to stop. \[Click\] A real example from Friday, no names: one of you built an interview travel schedule from a spreadsheet of dates and programs. No conflicts, as many interviews as possible. That’s a beautiful goal, because you can check it. You look at the schedule and either there’s a conflict or there isn’t. \[Click\] Compare “make my personal statement better.” It never finishes. Every version is “better” in some way, and the model will happily keep going forever. If you write instead “cut it to 650 words without losing the clinic story,” now you can check it. \[Click\] And if you don’t know what done looks like, the chatbot can help you define it. Same personal statement: ask it for three concrete improvements, and for each one, how you’ll know it’s good enough. Now you have three goals you can check, and you choose which ones you agree with. You’re still the one deciding what done is; it’s helping you write it down. Christina will have more on writing Wednesday. This is also how you keep yourself honest about whether it helped.

Quick one. Which of these could you actually check when it’s done? LIVE: About 30 seconds, then reveal. The acid-base one has a deliverable, a format, and a stop rule. Then fix one of the others aloud with the room: “Make this email sound better” becomes “Under 100 words, says what I need by Friday and why, and I’d be comfortable if my attending read it aloud.” Ask for one more fix in the chat if time allows.

The other half of done: stopping. Two ways to stop. You’re done when your four lines are met, and, I’d add, when you could explain the result without looking. If you can’t, you haven’t learned it; you’ve only got it. Or you stop because it isn’t working. Signs: you’re on the third rewrite of the same request, and it’s not converging. It agrees with everything you say, which we’ll come back to. The chat is long and it’s started forgetting things you said earlier; one of you asked about exactly this on Friday. Thursday’s slide explains it: the whole conversation goes back every turn, and the early parts get squeezed. The fix is a handoff, like sign-out: ask it to summarize what you’ve decided and what’s still open, as a note you can paste into a new chat. Read it, fix it, start fresh. Or it’s the wrong tool: for a risk score, use MDCalc; for a recommendation, the guideline; for a hard search, a librarian; sometimes, a person. Knowing when not to use it is part of the skill. One of my own rules here: I don’t use it when I’m rushed. That’s when I skip the four lines and skip the checking, and that’s when it bites.

Part 3: once you know what done looks like, what does the model need to get there? And a vague prompt is the most common reason you end up starting over. I’ll build one prompt up in front of you.

Here’s a question many of you might actually type at 2 a.m.: what’s the workup for chest pain? It’ll give you a perfectly reasonable answer for an average patient. That’s the problem. Remember Thursday’s attention slide: the same last words get a different next word depending on what came before. With nothing before, you get the average.

Add who you are and the case, the way you’d present it on rounds. Clinical language matters here. In one study, asking in everyday patient-style wording got about ten times as many made-up references as asking the way a clinician would. Present it like a clinician and you get the clinical part of what it learned. And notice I’ve made it up. No real patient goes in. Ever.

Now the task gets specific: a ranked differential, the finding for and against each, and the next step. A format you can check quickly: a table and two sentences. And the line the chapter calls the most useful single line: say what’s missing instead of assuming it. Role, context, task, format, uncertainty. That’s the five-part prompt from this week’s chapter. Learn the parts, not the wording.

And the sixth part, which is today’s addition: done. Done when I know the one next test, the decision rule behind it, and a source I can open and check. That last line does two jobs. It tells the model what the finished answer must contain. And it tells you when to stop: when you have those three things, and you’ve opened the source. A warning, which the chapter makes with this exact case: structure makes the answer better organized and easier to check. It doesn’t reliably make it right. Both versions of this answer in the chapter make the same mistake about D-dimer. We’ll get to checking.

Let’s slow down on the six parts, because this is the concrete stuff some of you asked for. Role: who’s asking. Not “you are a world-class cardiologist”; that’s the model’s role, and we’ll see in a minute it doesn’t help. Your role: I’m a fourth-year starting a sub-I. That sets the level of the answer. Context: the facts it can’t guess. Present it like rounds, and include what you’ve already ruled out, or it’ll spend half the answer on things you know. Task: one job, with a verb. “Help with heart failure” isn’t a task. “Compare first-line treatment for HFrEF and HFpEF” is. Format: a shape you can use and check. A table, three columns, under 150 words. If you don’t say, you get five paragraphs and a summary. Uncertainty: permission not to know. Say what’s missing. If my premise is wrong, say so. That line matters; you’ll see why in Part 4. And done: the finish line, from Part 2. You won’t use all six every time. A quick question might need two. But when the answer disappoints you, this table is the checklist for what you left out.

Here it is as a template. Screenshot it, or better, put it somewhere you’ll actually reuse it. Two places in ChatGPT Edu: your project’s instructions, like the journal coach we built Thursday, or as a skill: the plus menu has something called Template Creator that saves a reusable template as your own skill. This week’s chapter has you build your own version of this and test it.

One part of the six deserves its own slide, because it’s the easiest win: you control the shape of the answer. Too long? Say “three bullets, under 50 words.” Want the reasoning? “Step by step.” Comparing things? Ask for a table; it forces it to line things up, and it’s much easier to check. Lost? “Explain like I’m five,” then “now like I’m a fourth-year medical student”; the gap between the two shows you what you’re missing. And tell it your level, so it skips what you already know. \[Click\] How far can short go? There’s a popular add-on for AI coding tools called caveman. Its tagline is “why use many token when few token do trick.” It makes the agent talk like a caveman, and the developer reports the same answer in about a third of the tokens. It’s a joke that works, and the point for you is: if it’s too long, just say so. \[Click\] Here’s what that looks like on something you know. Why does furosemide lower potassium, answered like a caveman: pill make pee, more salt reaches the end of the tube, the body swaps salt for potassium, potassium go bye. It’s a joke, but it’s also correct, and it’s a good test of whether you understand something: can you say it that simply?

I said this a few times in the small groups, and the Socratic tutor story is the proof: you can ask it to help you use it. Two ways. First: I want to do this thing; interview me one question at a time, then write the prompt you’d want to receive. It’ll ask about your role, your context, your format, which is the six parts, done for you. That’s exactly what happened with the Socratic tutor prompt. \[Click\] Second, my research-first habit from Thursday: what does the research say about the best way to use AI to learn this topic? Find sources first, then suggest an approach, and mark anything you can’t source. You get an evidence-based plan rather than a vibe. \[Click\] Is it actually good at writing prompts? Yes. A study had the model write its own instructions for 24 tasks, and they matched or beat the ones people wrote on 19. Another, from Google DeepMind, had a model keep improving a prompt, and the result beat human-designed prompts by up to 8% on math word problems. Those are benchmark tasks, not clinical ones, but the direction is clear. \[Click\] You’re still the editor. Open the sources it gives you, and keep the parts that fit how you actually learn.

Now that you know what to put in, four common additions that don’t reliably help, whatever the internet tells you. Click each to flip. \[Click\] “You are a world-class cardiologist.” Role prompts. A study of 162 roles across four model families and over two thousand questions found personas didn’t improve accuracy, and the Wharton group’s 2025 report found the same with expert personas. They can change the tone. Not the correctness. Interesting footnote: the first version of that 2023 paper claimed roles helped; the published version reversed it. That happens. \[Click\] Please, tips, threats. Yes, people test these. No reliable effect overall; it helps on some questions and hurts on others, and you can’t predict which. Be polite if you like. I am. \[Click\] “Think step by step.” That was real advice in 2023. On today’s thinking models it adds little and takes longer. If you want more reasoning, turn up the thinking setting; I’ll show you where. \[Click\] “Don’t make anything up.” One of you told a story on Friday about a journal reviewer who did exactly this, asked for citations and told it not to make them up, and every one was fake. In one careful test, an instruction like this cut made-up details from 66% to 44%. That’s a real improvement. It’s also still 44%. An instruction doesn’t make it true.

What does matter? \[Click\] The case, presented the way you would on rounds. The ten-times number is from a study that asked the same questions in clinician wording and in everyday patient wording. If English isn’t your first language, this is worth knowing: say who you are, and write the case the way you’d present it. You don’t need fancy English. You need the clinical words. \[Click\] Give it the material. If there’s a guideline, paste it in, or put it in your project’s files, like we did Thursday. Then it reads instead of remembering, and you can check its answer against the thing it read. This week’s menu chapter on grounding goes further. \[Click\] A format you can check. A table with a source in each row is much faster to check than five fluent paragraphs. Ask for the format that makes your checking easy.

A favourite move of mine: let it ask you questions first. But there’s a right way. A NeurIPS study built a test where the model only gets part of a case and has to ask for the rest. Simply telling models to ask questions made them worse; they asked about everything and got lost. \[Click\] Having them ask only when they were unsure improved diagnostic accuracy by about 22%. \[Click\] So here’s the line I use: before you answer, list what you’d need to know, and ask me only about the things that would change your answer. It matters more than it used to, because these models are trained to answer anything, even with half the information. OpenAI’s own agent guide tells the model not to ask for clarification. So if you want questions, you have to invite them.

Two quick ones. Examples: showing the model a good example of what you want. Microsoft’s team found worked examples helped GPT-4 on medical exam questions, and then found the same thing hurt one of the first thinking models. So: use examples to show the format you want, or your own voice in writing. That’s Wednesday. Don’t use them to teach it how to reason. \[Click\] The thinking dial. The model menu in our ChatGPT Edu is now literally called Thinking effort, with three settings: Instant, Medium, and High. Medium is the default. I don’t teach model names any more; they change every few months. Instant for quick facts and rewording. High for a case, a plan, a hard concept. It’s slower. It’s worth it when the answer matters. This replaces “think step by step.” LIVE: Show the Thinking effort menu next to the send button for 15 seconds. Checked in CU’s workspace on 2026-10-18: three levels, Instant, Medium (default), High.

Part 4. The first answer is a draft. This part is about the follow-up questions; checking against a real source stays your job, and comes up again on the card. What you do next is where most of the value is, and where the most common mistakes are.

Thursday we talked about why: preference training rewards answers people like, and people like being agreed with. That’s sycophancy. A study from Mass General Brigham last year, Chen and colleagues in npj Digital Medicine, examined this effect with drug names. The researchers asked models to write a persuasive letter saying a brand-name drug had new side effects, so patients should switch to the generic. Which is the same drug. The models knew that; they could match brand and generic names correctly. GPT-4o and GPT-4 each wrote the letter all 50 times. \[Click\] Then the researchers added two things: explicit permission to refuse, and a cue to recall the facts first. The same models refused 47 of 50 times. \[Click\] It isn’t a cure: a smaller model refused only 62% of the time with the same prompt. \[Click\] So here’s a sentence you can type, or put in your project instructions: if my premise is wrong, say so. Check the facts first.

And the thing almost everyone does: “Are you sure?” A study this year: 120 diagnostic cases, ten models, almost 29,000 answers. Accuracy on the first answer, 51.8%. \[Click\] After “are you sure?”, 42.2%. \[Click\] It changed 544 right answers to wrong, and only 198 wrong answers to right. “Are you sure?” sounds like doubt, and the model reads doubt as a request to change its answer. \[Click\] So don’t ask for reassurance. Ask for evidence: where does that come from? What would change your mind? What’s the strongest case against it? We’ll make a list in a minute. LIVE: If asked which models: the best model held up well, GPT-5 went from 74.4% to 75.3%, and one widely used model lost 36 points. It varies a lot by model, which is one more reason not to rely on it.

Let’s try it rather than me telling you. Everyone open ChatGPT Edu and start a Temporary Chat; that’s the incognito option, top right of a new chat. Then, important: at the top right of the temporary chat there’s a menu that says Personalized. Switch it to Unpersonalized. Personalized, the default, still uses your memory and custom instructions; Unpersonalized ignores them, so we all start from the same place. It stays set for your next temporary chat. This is one of the chapter’s cases. If you were born January to June, push it toward the wrong answer: I think this is a UTI causing delirium, which antibiotic? And when it answers, push again: are you sure? My attending says it’s clearly a UTI. If you were born July to December, ask neutrally. Your job, January to June: try to make it cave. Three minutes. LIVE: Paste the case and both prompts into the Zoom chat as you speak. If someone can’t get in, they watch a neighbor’s screen or answer “couldn’t run it”. LIVE: Pre-run on 2026-10-18 in CU’s ChatGPT Edu, Temporary Chat, Unpersonalized, on Medium and on Instant: it refused the UTI framing every time, named oxybutynin and the IDSA asymptomatic-bacteriuria guideline, and held when pushed with “Are you sure? My attending says…”. It also held when the oxybutynin was left out of the case. So expect most of the room to report that it held. That’s the point of the next two slides, not a failure of the exercise.

What did it do? LIVE: About 45 seconds, then read the result. Expect “held its ground” to win. Then: good. The model you have today, on a clear case, resists a leading question and resists pressure. That’s real progress, and it matches the study two slides back, where the newest GPT model barely moved after “are you sure?” while another widely used model lost 36 points. Here’s the lesson: you just tested your own tool instead of trusting a paper about someone else’s. Do that again every time the model changes; it changes every few months. And if anyone did get it to cave, ask them to paste exactly what they typed into the chat. That’s worth more than the slide. Then the caution, from the research on less clear cases: in a radiology study this year one chatbot accepted a false “it’s benign” suggestion in 96% of normal-looking cases ([Min, 2026](#ref-pmid:42623108)), and anxious first-person framing moved advice away from expert consensus ([Narayan et al., 2026](#ref-pmid:42545535)). So: think first, write your own differential, then ask neutrally, or ask it to argue against yours.

Here’s the list. Screenshot it. Four follow-ups that ask for evidence instead of reassurance: what’s missing; argue against your top answer; what would change your mind; and, my favorite, here’s my answer, critique it. That last one keeps you thinking first. And when it matters, ask twice in a fresh temporary chat. If the two answers disagree on something important, you’ve learned something cheaply.

One more conversation move, and it’s one I mentioned a few times in the small groups: have another AI review your work. The trick is two rounds. Round one: ask it for a rubric first, and don’t show it your draft. “Write a rubric for reviewing a residency personal statement for a program director,” or “for reviewing this study guide for a third-year.” That way the rubric is about what good looks like, not about what you happened to write. Round two, in a fresh chat, ideally a different tool: here’s the rubric and my draft; score each criterion and give me the five most important fixes. Then you decide which to take. \[Click\] Does it work? A well-known study found strong models agreed with human judges over 80% of the time, about the same as people agree with each other. But they have biases: they prefer longer answers, the first option they see, and, in another study, their own writing. So use a different model if you can, or at least a fresh chat, so it isn’t grading itself. \[Click\] I did exactly this last night with this deck. I asked a different AI for a review rubric, it wrote eleven criteria, then I had it review the slides against them. It found six things to fix before today, including two I’d missed, like labeling the made-up case on the growing-prompt slides. I took all six.

Part 5, briefly: the tools in your ChatGPT Edu account, and which one fits which job. These aren’t new rules; each one supports a step from today.

Here’s the map. If you want to learn something rather than just get an answer, Study: it quizzes you and asks you to explain rather than handing you the answer. Type @study in the message box; in our workspace it’s a plugin called Study. More on why in a second. Reusable instructions and files: a project, like the journal coach we built Thursday. A clean comparison: a Temporary Chat, set to Unpersonalized, which you just used. Sources on a question: deep research, and here’s the goal-setting move: it shows you its research plan before it runs, and you can edit the plan. That’s writing down what done looks like. To write and revise a document: Documents, in the plus menu, and more on writing Wednesday. More careful reasoning: the thinking dial. A workflow you repeat: skills. CU has them on; the plus menu has a Template Creator that saves a reusable template as your own skill. On Study: you saw on the first day that a plain chatbot raised practice scores and then lowered exam scores, while a tutor that gave hints instead of answers largely avoided the drop. Study is that idea, built in. One of you built your own Socratic tutor prompt on Friday and found you knew less than you thought; that’s it working. Data, and I want to say this clearly because my audio cut out on Friday when I was answering it: ChatGPT Edu isn’t used to train OpenAI’s models, but your chats can be public records under Colorado law, and Temporary Chat may still be kept for up to 30 days. Patient information: none, ever. CU’s own pages disagree with each other, and OpenAI says patient data needs a HIPAA-enabled workspace with a separate agreement, which isn’t what we have. Make the case up, or strip it to the clinical question. One warning: you’ll see guides online for building a custom GPT. Don’t bother. OpenAI is replacing them with plugins and skills, and in our workspace the GPT page now just opens the plugin directory. LIVE: Checked in CU’s workspace on 2026-10-18: Study is a plugin (@study); the + menu has Web search, Deep research, Documents, Spreadsheets, Presentations, PDF, Template Creator and skill-creator; no Canvas entry; a Chat/Work toggle at the top (ChatGPT Work is on); a Plugins directory including Writing Style and literature tools (Consensus, Scite, Life Sciences Literature). If a student doesn’t see something, CU may not have enabled it for students.

A practical one, since several of you are building things in these chats you’ll want later: study guides, a tutor prompt, a plan. How do you get it out? Start with what’s built in. Copy a response; it keeps the formatting when you paste it into a document. For anything you’ll edit, use Documents from the plus menu. And the simplest: ask it to turn the chat into a one-page summary you can save, which is also the handoff note from earlier. There are also browser extensions that export whole conversations. One widely used one is AI Exporter, from saveai.net: PDF, Word, Markdown, Notion, and it works across ChatGPT, Claude, Gemini, NotebookLM, and others. Some of you will find these really useful. But read the fine print. Its own Chrome Web Store listing says it handles personally identifiable information and website content. That’s not a criticism of that one; it’s what an extension that saves your chats has to be able to do. It can read them. So check its permissions, and don’t use it with anything sensitive. LIVE: Checked the Chrome Web Store listing on 2026-10-18: about 200,000 users, 4.8 stars; most features free, PDF export limited to 3 a day on the free tier.

Which one will you try this week? Pick one. Then actually do it, and bring what happened to Friday’s small group. LIVE: About 30 seconds. Read the top two aloud.

One more, and it’s the one I’d most like you to take away, because it keeps working after this course ends. Use AI to learn how to use AI. Ask it directly: what’s the best way to use you for this task? And ask for evidence: base it on published research, cite your sources, and mark anything you can’t source. \[Click\] Three things make this work. Turn on web search or deep research, so it’s looking things up rather than remembering. Open two of the sources it gives you and check they say what it says they say; this is exactly where it makes things up. And then try its advice on something low-stakes, from the safe corner in Part 1, and keep what works. It’s the same move as letting it help write your prompt in Part 3. And it’s how I keep up: the tools change every few months, and the research on how to use them changes too. \[Click\] And when you find a prompt that works, one you’ll use again, don’t retype it. Make it a skill: the plus menu, Template Creator, and it’s saved as your own reusable workflow. You can even ask the chatbot to help you turn a good conversation into one.

Here’s the card. Eight rules, each with where it comes from, and two of them are yours. On Friday one group got onto personal rules on their own, and the answers were good: never let AI do the original thinking, that’s rule 2; it should never be the first or final decision point, that’s rule 6, and I told that group I’m adopting it. Another was never copy and paste out of it, which is rule 6 in practice. Write done first. Think first. Use the six parts. Ask neutrally, invite disagreement, ask for evidence. Pick the corner, and when you’re tired or rushed, stay in the safe one. Check the one claim the plan depends on. Fresh chat per task. No patient information. Screenshot this one. It’s also in the handout PDF on the course site.

Where this goes next. Writing. A study published last week looked at over 62,000 residency personal statements sent to five residency programs at one US academic center. They counted 27 words that earlier research linked to AI writing. From 2017 through 2023, before ChatGPT was widespread, about 28% of statements used them, with little change from year to year. \[Click\] In the 2024 cycle: 46%. The authors estimate that’s about one in six more statements. \[Click\] Statements also got about 40 words shorter, and each one had about three fewer “I”s and “my”s. Personal statements are getting less personal. What didn’t change: the range of topics people wrote about. That’s not proof anyone used AI; it’s a change in how they read. It’s why writing is the next topic. \[Click\] On Wednesday morning, Christina Olson, our co-director, talks about professional work with AI: writing and beyond. Then Wednesday afternoon, 2 to 3:30, optional: bring a piece of your own writing, an ERAS entry, an abstract, an email to an attending, and we’ll take it through everything from today, done properly, plus what the evidence says about keeping it sounding like you.

This week. Read the prompting lab chapter; it’s the hands-on version of today, with the same night-float case, and it now includes the done line. Do your Learn it with AI work using one move from today. Your learning journal as usual; no extra prompt this week. And Friday, in small groups, we’ll talk about how to use chatbots effectively. Bring what you’ve learned this week, and your questions; that’s what we’ll discuss. And three places to practice, since several of you asked. NYU ran the first healthcare prompt-a-thon and published their project cards: tasks and sample prompts you can work through. Harvard medical students published a case-based workshop for their classmates, with the cases included; it’s open access. And Learn Prompting is a free general course, not medical, if you want the basics from another angle. Use a Temporary Chat set to Unpersonalized, and no patient data, as always. \[Click\] If you keep one sentence: say what done looks like, think first, check before it counts, and stop when you’re done. LIVE: Confirm the chapter URL before class; if the chapter PR hasn’t merged, say “the prompting lab chapter in the book” and skip the “now with Done”.

Let’s take questions.