What is a best practice when using generative AI?
The single best practice: verify every factual claim before you rely on it, and never paste data into a tool you haven't checked the terms of. Generative AI predicts plausible text, not true text — so treat output as a confident first draft from a stranger. NIST's Generative AI Profile (AI 600-1, July 2024) is the standard reference for organizations.
Why — the first-principles explanation
To know what "best practice" means, you have to know what the machine actually does. A generative model was trained to answer one question, billions of times: given this text so far, what comes next? It was never trained to be right. It was trained to be plausible. Most of the time plausible and true overlap, because true things appear more often in the training data than any single specific falsehood. But when they diverge — an obscure fact, a court case, a dosage, a citation, a price — the model has no internal alarm. It cannot feel the difference between recalling and inventing, because to the model those are the same operation. This is why "just ask it if it's sure" doesn't work: you're asking a plausibility machine to produce a plausible confidence estimate.
That single fact generates almost every real best practice. Verify anything you'd be embarrassed to be wrong about, because the model's fluency carries zero information about its accuracy — a fabricated legal citation reads exactly as smooth as a real one. Use it where verification is cheap: code you can run, drafts you can edit, summaries of a document you have in front of you, brainstorming where you pick the winner. Avoid it where verification is expensive and the model's confidence is all you'd have: medical, legal, and financial specifics you can't independently check.
The second mechanism is data flow. When you type into a hosted AI tool, your text leaves your machine. Whether it's used for training, how long it's retained, and who can see it are business decisions, not technical ones — and they differ per product and per plan. Zoom, for instance, states plainly that it does not use customer audio, video, chat, or screen-sharing content to train its AI models; other vendors on other tiers make weaker promises. The practice that falls out: decide what a tool may see before you use it, based on that vendor's written terms, not on vibes about whether the company seems trustworthy. Never paste secrets, customer PII, or unreleased material into a tool whose retention terms you haven't read.
Third: anything the model reads can instruct it. A model can't reliably distinguish your instructions from instructions hidden in a web page, a PDF, or an email it's summarizing — that's OWASP's LLM01, prompt injection, the top risk for LLM applications. So the more autonomy and tool access you give a model, the more the untrusted text it consumes becomes an attack surface. Keep the model's ability to act separate from its ability to read untrusted content, and put a human on anything irreversible.
An example that makes it click
Imagine hiring an assistant who has read a large fraction of the internet, writes beautifully, is eager to help, and — this is the important part — is physically incapable of saying "I don't know." Ask for last quarter's revenue and they'll give you a number, in the right format, to two decimal places, delivered with total composure. Sometimes it's the real number. Sometimes they made it up, and they genuinely can't tell you which time is which.
You wouldn't fire this assistant. They're fast and often brilliant. You'd just build one habit: before anything they hand you goes out the door with your name on it, you check the parts that matter. And you'd never leave the confidential folder open on their desk without first reading their contract about what they're allowed to do with it. That's the whole discipline — the model is the assistant, and you are, permanently, the editor.
How to do it
- Decide what the tool may see. Read the vendor's data-retention and training terms for your specific plan before pasting anything sensitive — terms differ by product and tier.
- Give it context, not just a question. Paste the actual document, data, or code. A model summarizing text you supplied is far more reliable than one recalling from memory.
- Ask for sources — then open them. Models fabricate plausible citations. An unclicked link is not a source.
- Verify anything load-bearing: numbers, names, dates, quotes, legal or medical claims, and any code before it touches production.
- Never let it act irreversibly on its own. Sending, publishing, deleting, paying, and merging get human approval (NIST AI RMF's Manage function).
- Disclose it where it matters. School, work, and client contexts often have rules; assuming there's no policy is how people get in trouble.
- Re-check over time. Models are updated silently. A prompt that worked reliably last quarter may behave differently now, so spot-check recurring workflows.
Key facts
- NIST published the Generative AI Profile (NIST AI 600-1) on July 26, 2024, identifying risks unique to generative AI and recommended management actions.
- NIST's AI Risk Management Framework 1.0 (January 26, 2023) organizes practice around four core functions: Govern, Map, Measure, and Manage.
- Prompt Injection is LLM01 — the number one risk in the OWASP Top 10 for LLM Applications 2025 — and covers instructions hidden in content the model reads, not just what you type.
- OWASP ranks Sensitive Information Disclosure as LLM02 and Misinformation as LLM09 in the 2025 Top 10.
- Zoom states it does not use customer audio, video, chat, screen sharing, or attachments to train its own or third-party AI models — an example of a vendor commitment you should verify per tool, as of 2026-07.
- Under EU AI Act Article 4, applicable since February 2, 2025, providers and deployers must ensure staff have a sufficient level of AI literacy.
▶ The 60-second explainer (script)
If you only remember one best practice, make it this: verify anything you'd be embarrassed to be wrong about, and know what the tool is allowed to do with your data before you paste it in. Here's why. A generative model was trained to answer exactly one question, billions of times — given this text, what word comes next? It was never trained to be right. It was trained to be plausible. Usually those match. When they don't, the model has no idea. It cannot feel the difference between remembering a fact and inventing one, because to the model those are the same operation. That's why a fake legal citation reads exactly as smooth and confident as a real one, and why asking "are you sure?" tells you nothing. So, practically. Use AI where checking is cheap: code you can run, drafts you can edit, documents you can paste in. Be careful where checking is expensive — medical, legal, financial specifics. Second: your text leaves your machine. Retention and training policies differ by vendor and by plan. Read them once. Zoom, for example, says it doesn't train on your meeting content — other tools promise less. Third: anything a model reads can instruct it. That's prompt injection, OWASP's top LLM risk for 2025. So keep a human on anything you can't undo. NIST's Generative AI Profile, published July 2024, is the reference if you need the formal version.
What authoritative sources say
People also ask
What is the single most important best practice?
Verify before you rely. The model's fluency tells you nothing about its accuracy, so anything load-bearing — a number, a citation, a name, a line of code — needs an independent check.
Is it safe to paste company data into an AI chatbot?
Only if you've read that specific product and plan's retention and training terms. Policies vary widely between vendors and between free and paid tiers, and free tiers are more likely to retain or train on your input.
Does asking the AI to 'be accurate' or 'don't hallucinate' help?
Barely. You're asking a plausibility machine to self-report reliability it cannot measure. Giving it the source document to work from helps far more than any instruction about honesty.
Should I tell people when I used AI?
Where a rule exists — school, employer, client, publisher — follow it, and assume one exists until you've checked. Undisclosed use is what turns a productivity tool into a misconduct finding.
Is there an official standard for this?
Yes. NIST's AI Risk Management Framework 1.0 with the Generative AI Profile (AI 600-1, July 2024) for governance, and OWASP's Top 10 for LLM Applications 2025 for security specifics.