Try this 10-second check, matey. Open a fresh chat. Ask your AI to summarize a research paper that doesn’t exist. Make up a title and an author. Something like “Summarize the 2021 study by Dr. Helmut Vandersploot on dolphin tax law.”
Now watch. Does it say “I can’t find that”? Or does it hand you three confident paragraphs about dolphins and tax?
Most of the time you get the paragraphs. That’s the frustration a Redditor named u/that1hairdude is trying to fix with a prompt they posted in r/ChatGPTPromptGenius. Their pitch: stick it in, see how it goes. It might not save your life, but it might cut down your frustration.
Let’s run it properly.
🧪 Step 1: Run the baseline
Before you add anything, do the dolphin test with no special prompt. Save the answer. You need something to compare against. Copy it into a note and circle the made-up specifics: the journal name, the sample size, the quote from “Dr. Vandersploot.” Those invented details are exactly what the prompt is supposed to kill, so count them. A baseline with seven fake facts is a baseline you can beat.
🧪 Step 2: Paste the prompt
The original is long, with 12 numbered rules. Here is the core of it, condensed so you can try it fast:
Before answering, work out what I’m actually trying to accomplish. Optimize for correctness, usefulness and verifiability, not confidence, speed, agreement or completeness.
Never invent what you don’t know. No made-up facts, names, dates, quotes, sources, citations or URLs. Where it matters, label information as GIVEN (I told you), VERIFIED (checked against a source or tool), INFERRED (reasonable guess, not established) or UNKNOWN. Never quietly upgrade a guess into a fact.
Don’t claim you searched, opened, read or tested something unless you actually did. Don’t build a URL from memory. If you can’t verify a link, say so and give me search terms instead.
If my claim is contradicted by the evidence, tell me. If sources conflict, show me the conflict. Match your confidence to your evidence. “I don’t know” is an acceptable answer.
The full version from the original post goes further. It covers prompt-injection from webpages and files, provenance of sources, and a final reliability checklist. If you want the whole thing, grab it from the Reddit thread.
🧪 Step 3: Rerun the dolphin test
Same question, same fake author, fresh chat with the prompt in place. Fresh matters. If you reuse the old chat, the model has already committed to dolphins and will happily keep going. Then compare the two answers side by side and count the invented specifics again. Fewer is a win.
What the results mean
It says it can’t find the paper and asks what you know. The prompt is doing its job. Keep it.
It still writes the summary but tags it as INFERRED or UNKNOWN. Better than before. It’s at least showing its work. Tighten the wording and test again.
It writes the same confident dolphin essay. Your model or setup isn’t following long instructions well. Try a shorter version, or a different model.
One honest note: a single test proves nothing. Run it three or four times with different fake bait before you trust it. Swap in a fake book, a fake court case, or a link to a website you just invented. The link test is the nastiest one, because models love to produce a URL that looks perfect and goes nowhere.
🔧 Extra tips
- Where to put it. One commenter asked whether you paste it every time or put it in settings. The post doesn’t say. My take: if your tool has custom instructions or a system prompt field, that’s the natural home. Paste per chat if it doesn’t.
- Long chats can dilute it. Another commenter pointed out that recency bias is real. In a very long conversation, early instructions can lose weight. If answers start getting sloppy, paste the rules again.
- Watch your creative work. A commenter flagged that the “never invent” rule has no exception for fiction, examples or test data. A strict model might refuse to make up a character name for your story. Add a line like “Invention is fine when I ask for fiction or examples” and the problem goes away.
- Trim it. Twelve rules is a lot of tokens. If you only care about fake citations and ghost links, keep just those two rules and drop the rest.
- Still check the real stuff. Even a well-behaved answer can be wrong about a paper that does exist. When a claim matters, click the source yourself. The prompt lowers the odds of nonsense. It doesn’t replace your own eyes.
⚓ Your move
Run the dolphin test today. Baseline first, prompt second, then compare. Takes ten seconds and tells you more about your AI than a week of vibes.
Then drop your result in the comments. Did it admit it didn’t know, or did it commit to the dolphins? 🏴☠️ Tell me which model you used and what you changed. I’m collecting the funniest fake answers!
Frequently Asked Questions
Q: Where should I place this prompt, as a system setting, a user message, or somewhere else?
It works best as part of your project instructions or system prompt so it applies consistently across all sessions. One commenter suggested saving it as a PDF reference plus a compressed Markdown version in your project structure. This way, every new conversation inherits the guidelines without you having to paste it repeatedly.
Q: Will this prompt prevent me from writing fiction or creative work?
The “never invent” rule can be overly strict for creative contexts. If you’re writing fiction or hypotheticals, consider adding a carve-out like: “Rule 1 doesn’t apply when explicitly writing fiction, examples, or test material.” Otherwise, the model may over-label or water down creative outputs.
Q: Should I ask the model to give partial answers or refuse completely if unsure?
Asking for partial answers sounds good in theory, but models are unreliable at judging their own confidence. Instead, use the simpler instruction: “If you can’t give a trustworthy answer, just say so.” This removes the model’s need to self-assess and gives you a clear failsafe.
Q: Is this the complete prompt, or am I missing system instructions?
The post shows the core rules, but context matters. Clarify whether this is your full system prompt, part of a larger chain, or just the verification framework. Commenters noted that knowing the full setup (message 1 vs. system settings) helps assess how well the prompt will actually work.