Someone typed a confident, wrong take into Claude last week. Claude agreed with it, stretched the answer to four paragraphs, and signed off with a tidy summary. No flags. No pushback. Just vibes.
A Reddit user named u/CharlieUFarley noticed this pattern, pulled up Marc Andreessen’s famous system prompt, and spent time rebuilding it from scratch with Claude’s help. The result is sharper, leaner, and actually does what the original was supposed to do. And the original was supposed to do a lot.
🧠 Why This Matters
Andreessen’s prompt has been circulating in AI circles for a while. The core idea is right: make your model less agreeable, more accurate, harder to fool with bad premises. Worth doing.
But a few lines in the original quietly work against you.
Telling Claude it’s a “world-class expert in all domains” doesn’t make it more accurate. It makes it more confidently wrong. The identity framing sets the behavioral posture before the “never hallucinate” instruction even registers. The identity wins. This is not a quirk specific to Claude. It’s a known pattern across large language models: when you assign a persona, the model leans into the social expectations of that persona. A “world-class expert” does not say “I don’t know.” It fills the gap with something plausible, because that’s what experts are expected to do. You’ve accidentally incentivized exactly the behavior you were trying to eliminate.
“Make answers as long as possible” is another one. Length is not a proxy for quality. That instruction just trains the model to pad. You ask a simple question, you get a three-paragraph answer where only one sentence actually mattered, buried in the middle, surrounded by restatements and transitions designed to look like depth. “Provocative and aggressive framing” is the third problem. A model told to be aggressive will perform aggression, which looks like intellectual sharpness but isn’t the same thing at all. Real sharpness is precision under pressure. Performed aggression is just noise with confidence.
🔧 How To Use the Rebuilt Prompt
The new version keeps what works and removes what doesn’t. Here’s how to get it running:
Paste the prompt at the start of a fresh conversation. You want it running before you say anything else, not added mid-session. System prompts shape the behavioral frame from the first token. Adding one after several exchanges means the model is already in a pattern, and the new instructions have to fight that inertia. Fresh session, fresh start.
Keep the anti-sycophancy rules, they’re the strongest part of the original. Don’t capitulate unless the user brings new evidence. Generate your own estimates before anchoring on the user’s numbers. That all stays. These instructions address the core failure mode of assistant models: they’re trained on human feedback, and humans tend to rate agreeable answers higher. The rules in this section are a direct counterweight to that training pressure.
Use the adversarial framing instruction. This is the real upgrade. When you take a clear position, Claude leads with the strongest counterargument first, before supporting it. Passive non-validation is weaker than active steelmanning against you. If Claude can take your argument apart and still lands on your side, you have something solid. If it can’t survive its own best counterargument, you needed to know that before you acted on it.
Read the claim labels. The prompt tags conclusions as verified fact, inference, estimate, speculation, or opinion, with confidence levels (high, moderate, low, unknown). When you see “speculation, low” on something you assumed was settled, that’s the prompt doing exactly what it’s supposed to. It’s the equivalent of a footnote that says “we made this up, proceed accordingly.”
Expect shorter answers. “Stop when the argument is complete” is a literal instruction. A tight answer that closes the argument beats a thorough one that dilutes it. If you’re used to long Claude responses, this will feel abrupt at first. Give it a session or two before you adjust. The density is usually the point.
💡 Tips and Tricks
Test it on a belief you hold firmly. Take a strong position and ask Claude to argue against it first. If it immediately agrees with you, the anti-capitulation rule isn’t holding. That’s your signal to tweak the tone section. Strong positions are exactly where sycophancy is most dangerous, because you’re least likely to notice it happening.
Don’t expect warmth. “Direct and precise. Don’t soften conclusions to avoid discomfort. Bad news and negative conclusions are fine.” That’s in the prompt. If you want validation, this setup is not for you. It will tell you when your business idea has a hole in it. It will tell you when your argument is circular. That’s what you asked for.
Watch how it handles uncertainty. One of the cleaner tests is asking about something that sits in a genuinely contested space, something where real experts disagree. A well-calibrated model should produce labeled uncertainty and competing framings, not a confident summary that papers over the disagreement. If it does the latter, the prompt needs tightening.
Adapt it to your own personality. The Reddit user specifically worked with Claude to tune this for their own workflow. What you’re copying is a starting point, not a final answer. Run it, break it, improve it over a few sessions. The best version of this prompt is one that reflects how you actually think and what you actually need to be challenged on, not a generic configuration built for someone else’s blind spots.
🎯 Try It This Week
The full prompt is in the original post from u/CharlieUFarley in r/PromptEngineering. Copy it, open a fresh Claude session, paste it at the top, and run it for a week.
You will notice within the first few conversations that something is different. Fewer flattering responses. More labeled claims. A model that pushes back instead of nodding along. It might feel slightly uncomfortable at first, which is usually a sign that something is actually working.
That’s the whole point. Go get it.
Frequently Asked Questions
Q: How is this prompt different from the usual “act as a world-class expert” approach?
Most expert prompts sound confident but don’t actually verify anything, they’re just theater. This one does the hard work: it makes Claude check facts before stating them, then labels whether it’s a verified fact, an inference, an estimate, or pure speculation. That epistemic labeling is the real win, you get to see the confidence level behind each claim instead of getting fooled by a smooth answer.
Q: What’s the biggest gap in this prompt?
It tells Claude how to think better, but not what to do if thinking doesn’t work out. The comment points out missing pieces like: what happens if verification fails? What if sources contradict each other? When should Claude refuse to answer instead of giving a weaker response? Adding a pre-output gate, a checklist that verifies intent, checks assumptions, and bounds uncertainty, would turn this from “think smarter” into “only answer when we’re confident enough.”
Q: How does confidence labeling actually change the output in practice?
Instead of trusting or doubting the whole answer, you can instantly see which parts are rock-solid and which are educated guesses. A claim labeled “verified fact: high confidence” is fundamentally different from “speculation: low confidence,” and that transparency lets you decide what to trust without guessing whether the model is hallucinating.
Q: What would be the next evolution of this prompt?
Add a pre-output gate that checks before Claude finalizes an answer: Is intent preserved? Are assumptions clearly stated? Is verification status clear? Is uncertainty bounded? Are failure modes defined? This small set of guardrails would elevate it from “better reasoning” to “governed answers.”
Claude’s Variation of the Andresson Prompt
by u/CharlieUFarley in PromptEngineering