How to Prompt for Accuracy in Generative AI: Constraints, Quotes, and Extractive Answers

Generative AI doesn’t guess. It predicts. And when it predicts wrong, it doesn’t say "I don’t know." It confidently makes up facts, invents citations, and serves up half-truths dressed like facts. This is the hallucination problem. You ask for the best restaurant in Cambridge, and it picks one in England when you meant Massachusetts. You ask for a summary of a medical study, and it cites a paper that never existed. You’re not alone. Millions of people are using AI tools every day without realizing how easily they’re being misled.

Here’s the truth: you can’t fix AI. But you can fix your prompts.

Most people treat AI like a search engine. Type in a question, get an answer. But AI isn’t a database. It’s a pattern-matching machine that stitches together fragments of what it’s seen before. If you give it vague input, you’ll get vague, unreliable output. The difference between a useful answer and a dangerous lie often comes down to how you ask the question.

Stop Being Vague. Start Being Specific.

"Write a report" is a terrible prompt. So is "Tell me about diabetes." These are empty shells. AI fills them with whatever seems plausible - which often means it makes stuff up.

Try this instead: "Summarize the 2023 CDC guidelines on Type 2 diabetes management for adults over 65, focusing on medication interactions with blood pressure drugs. List only sources from peer-reviewed journals published after 2020. Exclude lifestyle advice."

That’s not just a prompt. It’s a blueprint. It tells the AI: What to do, who it’s for, what sources to use, and what to ignore. The more specific you are, the less room the AI has to hallucinate.

Real-world example: A team at UCSF asked AI to build a model predicting preterm birth using data from 1,000 pregnant women. One prompt said: "Write code to identify risk factors for preterm birth from this dataset." Result? Garbage. Another prompt said: "Use Python and scikit-learn to train a logistic regression model. Include only variables from the DREAM Challenge dataset: gestational age, maternal BMI, prior preterm birth, and preeclampsis diagnosis. Output AUC score and feature importance. Do not use neural networks." The second prompt generated working code in minutes - code that matched human-built models. The difference wasn’t the AI. It was the prompt.

Use Constraints Like a Fence

AI doesn’t have ethics. It doesn’t have common sense. It just follows patterns. That’s why you need to build fences around what it can and can’t do.

Use "Do" and "Don’t" statements like rules in a game:

• Do: Use only data from PubMed Central articles published between 2020 and 2025.
• Do: Format the answer as a bulleted list with no more than five items.
• Don’t: Invent statistics or estimate values not present in the source data.
• Don’t: Mention any brand names unless explicitly provided.

These aren’t suggestions. They’re guardrails. In biomedical research, one team found that prompts with explicit "don’t" rules reduced fabricated citations by 68%. Why? Because the AI had a clear boundary. It couldn’t wander into "probably true" territory - it had to stay inside the lines.

Even simple constraints help. If you ask for a quote, say: "Quote the exact sentence from the source text. Do not paraphrase." This forces the AI to retrieve, not rewrite. It’s the difference between hearing a witness and reading a fanfiction version of their testimony.

Act Like a Professional - Not a Chatbot

When you say "act as a doctor," the AI doesn’t become a doctor. But it does shift its internal weighting. It starts pulling from medical training data, prioritizing clinical language, and avoiding casual phrasing.

Try: "Act as a senior biomedical data scientist reviewing a draft manuscript. Critique the statistical methods used in the study. Point out any inconsistencies with the DREAM Challenge protocols. Use formal academic tone. Do not suggest new experiments."

This works because AI responds to role cues. It’s not pretending - it’s accessing a different set of patterns. A study from Wayne State showed that prompts framing the AI as a "peer reviewer" produced more accurate, critical feedback than prompts that just said "review this."

Same goes for legal, financial, or technical roles. "Act as a patent attorney" will make the AI focus on precise terminology, jurisdictional limits, and claim structure. "Act as a financial auditor" will make it flag inconsistencies in ratios and trends. You’re not tricking it. You’re guiding it to the right mental library.

Extract Exact Answers - No Paraphrasing

One of the biggest risks in AI output is paraphrasing. The AI takes your source, rewords it, and slips in a tiny lie. You think you’re getting a summary. You’re actually getting a rewritten version with errors baked in.

To get extractive answers - real quotes, real numbers, real references - you need to lock it down:

• "Quote the exact sentence from the original text that describes the primary outcome."
• "List the three most frequent comorbidities mentioned in the study. Do not add any that aren’t explicitly stated."
• "Copy the exact wording of the inclusion criteria from Table 2. Do not summarize."

These prompts force the AI to behave like a copy machine, not a storyteller. In the UCSF study, prompts with "copy exactly" instructions reduced factual errors in extracted data by 72% compared to open-ended summaries.

And here’s a pro tip: if you’re pulling quotes from a long document, give the AI the text itself. Don’t just say "summarize this paper." Paste the relevant paragraph. Say: "From this text, extract the sentence that defines the primary endpoint. Highlight it in bold."

Iterate Like a Conversation

No one writes the perfect prompt on the first try. Even experts don’t. The best users treat AI like a junior colleague - one who’s smart but needs clear direction.

Start with a basic prompt. Get the output. Then say:

• "That’s close. I need the data broken down by age group."
• "You mentioned a study from 2022. Can you find the DOI?"
• "I don’t see the confidence interval. Can you add it?"
• "You used "likely" - that’s too vague. Use exact percentages from the source."

This feedback loop is where accuracy happens. Each correction teaches the AI what you value. It’s not magic. It’s training - with you as the instructor.

One researcher at Harvard described it this way: "I used to think I needed to get it right the first time. Now I know I need to get it right in five tries. The AI doesn’t mind. I do."

What You Can’t Fix

Even the best prompts can’t fix broken AI. Harvard’s guidelines are clear: "AI-generated content can be inaccurate, misleading, entirely fabricated, or offensive." No amount of prompting eliminates that risk.

That’s why human review isn’t optional - it’s the final filter. Always check:

• Are the sources real? (Search the DOI or title yourself.)
• Do the numbers add up? (Cross-check with the original data.)
• Is the tone appropriate? (Does it sound like a scientist, or a marketing bot?)

There’s no substitute for skepticism. The most accurate AI in the world still hallucinates. The difference between success and disaster is whether you’re watching.

Meta-Prompts: Ask the AI How to Ask Better

Stuck? Don’t guess. Ask the AI for help.

Try: "What information do you need from me to give me an accurate answer about this topic?" or "How should I phrase this prompt to get the most reliable results?"

This meta-prompting technique works because AI often knows what it can’t do. It can tell you: "I need the full text of the study," or "I can’t access data from 2026 because my training ends in 2024."

It’s like asking a librarian: "What do I need to bring to find this book?" You’re not just getting an answer - you’re learning how to ask better next time.

One team at UCSF used this approach to cut their prompt development time in half. Instead of guessing, they asked the AI to coach them. The result? More accurate outputs, faster.

Final Rule: Accuracy Is a Practice, Not a Feature

Prompting for accuracy isn’t a trick. It’s a skill. Like writing a good email or designing a spreadsheet. It takes practice. It takes patience. And it takes the willingness to admit: "I don’t trust this yet. Let me try again."

Forget the hype. The real power of AI isn’t in how smart it is. It’s in how much you can teach it - one precise, constrained, quoted, corrected prompt at a time.

And if you’re using AI for anything that affects decisions - health, law, finance, policy - your job isn’t to believe what it says. It’s to verify it. Every time.

Use AI like a smart assistant - not a oracle. It’s fast, but fallible. Your job isn’t to trust it. It’s to train it, test it, and never stop checking.