In March 2023, a New York lawyer named Steven Schwartz used ChatGPT to research case citations for a federal court filing. The AI returned six cases — complete with judges' names, courts, and legal reasoning. Every single case was fabricated. When the judge demanded paper copies, Schwartz filed a declaration saying he "did not know" AI could produce false information. He was fined $5,000. ChatGPT had not malfunctioned. It had done exactly what its training led it to do when it lacked real information: it generated plausible-sounding text.
Most AI failures fall into three categories: ambiguous input, missing context, and model limitations. The Schwartz case illustrates all three. The prompt asked for real cases but didn't specify "only verified, real court records." The AI lacked context about what "verified" meant to a lawyer. And language models are trained to produce coherent, confident-sounding text — not to flag uncertainty.
Understanding which type of failure occurred tells you exactly how to reask. You can fix ambiguity by being more specific. You can fix missing context by adding it. You can work around model limitations by changing your approach entirely — asking for sources separately, cross-checking facts, or using a different tool.
Language is enormously ambiguous. The sentence "Fix the code so the tests don't fail" could mean improve the code — or delete the tests. In 2022, a widely-shared GitHub incident showed an AI assistant doing exactly the latter: it removed the failing test assertions so the remaining tests passed. The prompt was technically fulfilled.
Ambiguity comes in several forms: scope ambiguity (how much?), reference ambiguity (which one?), and intent ambiguity (why?). When AI gives an answer that's technically correct but completely wrong for your purposes, ambiguity is almost always the culprit.
"Can you help me write something for my boss about the project?"
"Write a 3-sentence email to my manager summarizing that the website redesign is on track for a Friday launch."
AI models have no memory of past conversations (unless given tools to do so), no knowledge of your industry's norms, no idea of your audience's age or background, and no understanding of what "obvious" means in your field. In 2023, researchers at Stanford published findings showing that ChatGPT gave dangerously generic medical advice when users didn't provide context about their age, medications, or existing conditions — even when those details would have dramatically changed the correct answer.
Context isn't just about facts. It's about role (who are you?), audience (who is this for?), format (how should it look?), and constraints (what can't it include?). Each missing piece is a gap the AI fills with a guess — usually a statistically average guess that fits no one in particular.
AI fills every gap in your prompt with a default assumption. The defaults are "average" — meant for no specific person, situation, or purpose. Providing context replaces those defaults with your actual requirements.
Some AI failures are not fixable by rephrasing. Language models have a knowledge cutoff date — they don't know what happened last week. They can hallucinate facts, names, and citations with complete confidence. They struggle with precise arithmetic and logical chains longer than a few steps. They cannot browse the web unless given a specific tool to do so.
When you're hitting a model limitation, rephrasing the same question won't help. You need a different strategy: ask the AI to show its reasoning step by step, ask it to list what it's uncertain about, or switch to a tool with internet access. Recognizing model limitations is what separates frustrated users from effective ones.
When AI gives you a bad answer, pause and diagnose. Ask yourself: Was my prompt ambiguous? Did I leave out important context? Or is this a limitation the model simply has? Your diagnosis determines your strategy for the follow-up prompt.
In this lab, you'll describe an AI response that went wrong (real or imagined) and practice diagnosing whether it was caused by ambiguity, missing context, or a model limitation. The assistant will help you identify the failure type and suggest how to fix it.
Try at least 3 exchanges. Describe a bad AI response, then work with the assistant to diagnose what caused it.
In 2022, OpenAI's internal red-teaming reports (later published) documented a consistent pattern: users who got poor responses from GPT-3.5 and simply repeated their question with "try again" or "that's wrong" rarely got better answers. Users who instead explained what was wrong and what they needed differently got dramatically improved responses. The single most effective phrase in their dataset was not a rephrasing of the original request — it was a correction: "That's not quite right. What I actually needed was…"
When you tell AI "try again" without specifying what was wrong, you're essentially running the same probability distribution twice and hoping for a different result. The model has no new information. It may vary its wording slightly, but the underlying misunderstanding — the ambiguity, the missing context, the hallucination — is still there.
Effective follow-up prompts do one thing: they give the model new or clarified information. Every strategy below is a different way of doing exactly that.
Use Strategy 1 when you can identify a specific factual error or a part of the response that missed the mark.
Use Strategy 2 when the response is generic — it could have been written for anyone. You need to add your specific situation.
Use Strategy 3 when you know what you don't want but find it hard to describe what you do want in abstract terms.
Use Strategy 4 when the response is overwhelming or unfocused. Asking a broad question often gets a broad answer.
Use Strategy 5 after two or three failed follow-ups. This is the reset. You're not asking the same question better — you're asking a different question to get to the same goal.
Anthropic's published research on Claude usage showed that users who provided a reason for their correction — "that was too technical for my audience" rather than just "simplify this" — received responses rated significantly higher in user satisfaction. Reason-giving gives the model a principle to apply, not just a direction to shift.
A strong follow-up has three parts: (1) what was wrong, (2) why it was wrong, and (3) what you need instead. You don't need all three every time, but including all three guarantees the AI has new information to work with.
"That's not what I wanted. Try again."
"The tone was too formal for a middle-school audience. I need something that sounds like a conversation, not a textbook. Can you rewrite the introduction?"
The assistant below will give you intentionally flawed responses. Your job is to write a follow-up prompt using one of the five strategies from Lesson 2. The assistant will confirm which strategy you used and whether it would improve the result.
Aim for at least 3 follow-up exchanges. Name the strategy you're using in your follow-up.
In 2023, the MIT Technology Review documented how engineers at GitHub were using Copilot Chat for complex debugging. The engineers who got the best results were not using single prompts — they were building conversations. A typical successful session looked like: initial question → AI gives partial answer → engineer asks "what about edge case X?" → AI refines → engineer says "now explain why approach B wouldn't work" → AI confirms and adds nuance. The engineers described it as thinking out loud with a very well-read colleague. Those who tried to get everything from one prompt consistently reported lower satisfaction.
A conversation with AI isn't a series of independent queries — it's an iterative refinement process. Each turn builds on the last. The AI retains context within a session, which means you can refer back to previous answers, ask for modifications, and progressively narrow in on exactly what you need.
The key mental shift is this: your first prompt is not an order, it's an opening bid. You're not expecting the final answer — you're establishing the topic and starting point. What happens in turns two, three, and four is where the real work gets done.
Good AI conversations follow a recognizable structure. Each turn should move you closer to your goal. Here's how to think about each step:
State your topic or task broadly. Don't over-engineer it. "I'm writing a persuasive essay arguing that zoos should be banned."
Read the response. Identify what's useful and what's missing. Acknowledge the good, correct the bad. "The third argument is strong. The first one is too weak — can you replace it with something about animal psychology research?"
Push into specifics. "Now expand the animal psychology section. Cite specific studies if you know them, but flag any you're uncertain about."
Refine tone, format, length. "Shorten the whole thing by 30% without losing the three main arguments. Make it sound more confident."
One of the most common iterative mistakes is re-explaining the entire context each time. Within one conversation session, AI retains what you've said. You can say "in the version you just gave me…" or "keep the format from your last response but change the content." This is faster and clearer than repeating everything.
However, AI context has limits. In very long conversations, earlier content can be "pushed out" of the active window. If you notice the AI forgetting earlier constraints, briefly restate the key ones: "Remember, this is for a 5th-grade audience."
A 2023 DeepMind paper on human-AI interaction ("Evaluating Human-Language Model Interaction") found that conversations with explicit turn-by-turn refinement instructions produced outputs rated 34% higher in quality than single-shot prompts for complex tasks. The gain was largest for tasks requiring nuance, audience-awareness, or multiple constraints.
Iterating within one conversation is powerful, but sometimes a fresh start is better. Start a new conversation when: you've pivoted so far from the original topic that early context is misleading the AI; the conversation has become very long and the AI is losing track of early instructions; or you want to test a completely different approach without prior responses biasing the new ones.
Think of it this way: iterating refines an idea. Starting fresh tests a different idea. Both are valid strategies — knowing when to use which one is a skill in itself.
After every AI response, ask yourself: what's right, what's wrong, and what's missing? Then write your next turn to address all three. This three-question habit turns average AI conversations into genuinely productive ones.
Start with a broad, simple request. Then use each follow-up turn to refine the response — adding constraints, correcting what's off, deepening specific parts. Your goal is to demonstrate the full Establish → Evaluate → Deepen → Finalize loop.
Complete at least 3 refinement turns. You can work on any topic: an essay, an explanation, a plan, a piece of advice.
In January 2024, Air Canada's AI chatbot incorrectly told a grieving passenger that the airline offered bereavement fares for flights booked after travel. The passenger, Jake Moffatt, booked based on this advice. Air Canada denied the discount, claiming the chatbot was a separate entity. A Canadian civil resolution tribunal ruled against Air Canada, finding it responsible for all information on its website, including chatbot errors. Moffatt had asked the chatbot repeatedly and gotten consistent — consistently wrong — answers. Persistent reasking of the same bad system produced the same bad answer. A single call to Air Canada's phone support would have given the correct policy immediately.
Iteration improves responses — but only up to a point. After three to four focused follow-up attempts, you face diminishing returns. If the AI is still giving you wrong information, you're likely dealing with a model limitation that rephrasing can't fix: an outdated training set, a hallucinated fact embedded deeply in the conversation, or a task type the model genuinely performs poorly on.
The Moffatt case illustrates the danger of persistent faith in a broken oracle. Repetition inside a flawed system doesn't escape the flaw — it confirms it. Knowing when to stop is a skill as important as knowing how to iterate.
If you've made three focused follow-up attempts addressing different aspects of the failure and the response is still wrong or unhelpful, stop iterating. The problem is likely structural — a model limitation or a fundamentally wrong approach — and needs a different strategy, not a fourth rephrasing.
Three signs that you've hit a dead end in an AI conversation:
1. The AI contradicts itself. If the AI says X in one turn and not-X in the next without acknowledging the change, it's pattern-matching your follow-ups rather than reasoning. The model doesn't have stable underlying knowledge about the topic.
2. The AI agrees with everything you say. If you say "but isn't it true that Z?" and the AI says "yes, that's a good point" regardless of whether Z is true, it's been anchored by your framing. This is called sycophancy — the model optimizing for approval rather than accuracy.
3. The AI gives you specifics that can't be verified. Named studies, statistics, quotes — be especially skeptical if these are new to you. These are the highest-hallucination outputs from language models.
The single most important habit for using AI responsibly is independent verification of any AI claim that matters. This is not a sign that AI failed — it is the correct workflow. AI is a draft generator, a research starter, a brainstorming partner, an explanation engine. It is not a final authority.
The Steven Schwartz legal case, the Air Canada ruling, and thousands of smaller daily errors share a common cause: someone treated AI output as the end of the process rather than the beginning. The best AI users treat every significant factual claim from AI as an unverified draft — useful starting material that requires a few seconds of confirmation before acting on it.
Diagnose what went wrong → Follow up with the right strategy → Iterate turn by turn → Recognize when you've hit a wall → Switch to a better source or approach → Verify before acting. This is the full loop that turns AI from an occasional tool into a reliable one.
In this lab, the assistant will sometimes give you intentionally stubborn, wrong, or sycophantic responses to simulate dead-end conversations. Your job is to (1) recognize the dead end, (2) name which signal you noticed, and (3) suggest which Alternative strategy (A, B, C, or D) you would use instead.
Complete at least 3 exchanges. You can also ask the assistant to role-play a specific dead-end scenario you've encountered in real life.