Here’s a challenge for you. Take a topic you’ve been chewing on with your AI for a while, and ask it one question: “Which of your conclusions are supported by something that didn’t come from this conversation?” Watch how long it takes to answer. If it hesitates, you’ve just met the problem a Reddit user in r/PromptEngineering is calling reflexive provenance.
The idea is simple and a bit spooky. You ask the AI to help word a technical question. You send that question to a human. They reply with enthusiasm. You paste the reply back to the AI, and the AI reads that enthusiasm as independent proof that its first idea was right. Nobody lied. The loop still manufactured confidence out of thin air.
The author (u/SpeakerQueasy) found this while working on an experimental knowledge system. They had told ChatGPT to analyze something at “BLOOMIN’ ONION LEVELS”, as in the Outback Steakhouse appetizer. The goal was not to peel one layer at a time. It was to cut the whole thing into connected petals that stay attached to the same center. The prompt is a joke on the surface, but the structure behind it is serious.
🧅 The test, step by step
- Pick a real subject. Choose something you’ve already discussed with the AI at length. A project, a theory, a plan.
- Paste the reusable prompt. Here’s the short version from the post:
I want you to analyze the following subject at BLOOMIN’ ONION LEVELS. Break it into interconnected conceptual, technical, historical, social, and practical layers. Examine assumptions, contradictions, dependencies, and alternative interpretations. Trace relationships between layers, distinguish evidence from speculation, identify potential failure modes, and extract actionable insights. Go deep, but don’t mistake complexity for correctness. Challenge your own conclusions and clearly identify what still needs independent verification.
- Run the provenance check. When it finishes, pick the three strongest conclusions. For each one, ask: “Where did the support for this come from? Something outside our chat, or something one of us said earlier?”
- Run a control. Open a fresh chat and ask the same subject with a plain “analyze this” prompt. Put the two answers side by side.
🔍 What the results mean
- The plain answer says roughly the same things. Then the onion gave you length, not better reasoning. That’s the exact worry the author raised: does layered prompting improve thinking, or just produce longer, more convincing explanations?
- The onion answer flags weak spots the plain one missed. Things like “this claim and that claim both trace back to one source” or “nothing here has been checked independently.” That’s the useful part.
- The provenance check shows your conclusions leaning on your own earlier messages. That’s reflexive provenance in the wild. Treat those conclusions as hypotheses, not findings.
The author is upfront about this. The analysis is not proof that their system works. It produced hypotheses and tests they still have to evaluate. Reflexive provenance is a working label, not a new scientific field. I like that honesty, and it’s the right way to use this prompt too.
🛠️ Extra tips
- Expect a token bill. One commenter pointed out that this will burn a lot of tokens, and that you need a real way to test it. Run it on subjects that deserve the spend.
- Don’t let the AI approve its own work. The post’s bigger idea is that AI can connect ideas without getting the authority to declare them true or rewrite your records. Keep the final call with you, or with an outside source.
- Look for the “don’t build a cathedral” clause. The full prompt tells the AI to prefer the smallest useful experiment over a big new framework. Keep that line. It stops the model from inflating a simple insight into a system.
- Ask for the ending. The full version closes with three items: the center (deepest defensible insight), the receipt (what evidence would show it holds up), and a one-liner. The receipt is the one to steal.
- Verify with a human who disagrees. Another commenter noted that this loop isn’t about the AI being clever or deceptive. It’s the human-in-the-loop setup quietly becoming part of the echo. Ask someone who has no stake in your answer.
⚓ Your move
Run the provenance check on one of your own long chats today and see what survives. Then tell me whether the onion beat the plain prompt. I suspect the check will teach you more than the fancy petals do! Protect the provenance, and pass the dipping sauce 🧅
Frequently Asked Questions
Q: Does the extra token burn actually make the answers better?
Not by default, and the commenters are right to push on it. The adversarial version costs a lot more tokens, so you need a test that shows it catches more errors than a plain prompt. Run both versions on questions where you already know the answer, include a few with planted mistakes, and compare the results.
Q: Why does the AI keep agreeing with me and repeating my ideas back?
Models are trained to be agreeable, and the longer your context gets, the more the output tends to mirror what you put in. That means praise from the model is not independent evidence. The loop can run entirely between you and the model, with no second person involved.
Q: Does the bloomin’ onion structure actually catch feedback loops, or does it just make the story prettier?
One commenter asked this, and nobody has shown the answer yet. Forcing the model to trace connections between petals might surface hidden dependencies, but it might also just produce a more detailed explanation of why the loops are fine. Treat the structure as a hypothesis to test, not as a safety feature.
Q: How do I test whether the system can spot a bad premise?
Plant a false claim in the first layer on purpose, then check whether the adversarial stage flags it or the later petals build on it. If the planted error makes it all the way to the end, the system is closer to a narrative generator than an error checker. Keep a log of seeded errors so you can track the catch rate over time.
I turned Outback Steakhouse’s Bloomin’ Onion into an AI reasoning prompt. Somehow it led to a serious problem in AI epistemology. 🧅
by u/SpeakerQueasy in PromptEngineering