Most people write prompts the way they write emails. Full sentences. Context. A friendly opener. Maybe a “thanks in advance” at the end.
A Reddit user called u/AdCold1610 stumbled onto something better by accident. He was tired, trying to stretch his free tier, and typed: “fix bug. line 47. null error.”
Claude fixed it. Same quality. One fifth of the tokens.
He posted about it on r/ChatGPTPromptGenius, picked up 256 upvotes, and named it the Caveman Theory. The idea: AI models don’t need pleasantries. They need information. Just information. The post spread because it hit something people had been feeling but never actually tested. Everyone has written a polite prompt. Almost nobody has measured what the politeness cost them.
The Contrast Is Stark
Here’s what a typical prompt looks like when someone hits a bug:
“hey Claude, i’ve been working on this project and i’m running into an issue with the function on line 47, it keeps throwing a null error and i’m not sure what’s causing it, could you take a look?”
57 words. One actual piece of information buried inside. The rest is noise that exists for your comfort, not for the model’s comprehension. The AI doesn’t need to know you’re running into something. It needs to know what the something is.
The caveman version: “line 47. null error. fix.”
4 words. Same output. You just paid tokens to say please to software.
Multiply that pattern across ten prompts a day and you’re burning 30 to 50% of your context window on social lubricant the model never needed. On free tier, that’s conversations you can’t have later. On paid, that’s a dollar figure attached to politeness.
The Framework 🪨
The original post breaks it down into five rules:
- 🚫 No greetings. Claude doesn’t have mornings. “Good morning, I was wondering…” is dead weight. Start with the task. The model processes information, not rapport.
- 🚫 No apologies. “Sorry if this is a weird question” is five words of pure credit waste. Just ask. There are no weird questions, only inefficient ones.
- 🚫 No backstory. “I’ve been working on this for a while and…” Claude needs the what, not the history of the what. If context is genuinely relevant, include only the part that changes the answer. Cut everything else.
- 🚫 No closing remarks. Every “thanks so much, this was really helpful” is money going nowhere. Gratitude is a human instinct. The model doesn’t register it. Save it for people.
- Verbs and symbols only. “A vs B?” instead of “can you compare option A versus option B.” It knows what you mean. The parser is sophisticated enough to handle telegraphic syntax. Use it.
Real Swaps That Work
Before: “could you help me make this email sound more professional and formal while keeping the core message intact”
After: “email. more formal. keep meaning.”
Before: “what’s the best approach to structuring a landing page for a SaaS product targeting small business owners”
After: “SaaS landing page. small business. best structure.”
Before: “I have a list of customer feedback and I need to understand what the main themes and complaints are across all of them”
After: “customer feedback list. main themes. top complaints.”
The output quality holds. The token count collapses. The pattern works across categories: code, copy, research, summarization. Any task where the goal is clear and the context is already sitting in front of you.
When It Breaks
Caveman Theory has a real limit. Complex creative work, writing in a specific voice, emotionally nuanced tasks, these need real context. Vague input produces vague output, and the method doesn’t fix unclear thinking. If you don’t know what you want, shortening the prompt doesn’t help. It just produces a shorter version of the wrong thing.
The rule of thumb: if you could explain the task to a smart stranger in one sentence, Caveman Theory works. If the task requires the stranger to understand your brand voice, your audience, your format preferences, or the broader project context, give them that context. Just give it efficiently. “Blog post. tone: direct, no fluff. audience: founders. topic: [X]. 600 words.” Still short. Still specific. Still right.
But for clear, task-based instructions? The original post estimates that covers roughly 70% of daily AI use. Debug this. Summarize that. Rewrite this shorter. Those tasks don’t need ceremony.
Why Nobody Told You This
The product doesn’t benefit from you being efficient. You burn more tokens, they make more revenue. You figured this out or you didn’t.
There’s also a psychological layer here. We’re trained from childhood to be polite, to soften requests, to add social cushioning before asking for something. That instinct runs deep. Applying it to software is just habit misfiring in a new environment. The model is not your colleague. It has no feelings about how you phrase things. It only has the information you give it and the instructions you set.
If you’re on free tier, wasted words are messages you won’t get to send later. If you’re on paid, wasted words are real money. Either way, politeness has a price here that it doesn’t have anywhere else.
Start writing prompts like telegrams. Task. Context. Format. Send. The AI will meet you there.
Frequently Asked Questions
Q: Doesn’t fuller context actually produce better results?
Sometimes, richer context helps Claude tailor output and enable better follow-ups, especially for complex or nuanced requests. But for straightforward tasks (bug fixes, quick rewrites), minimal prompts produce identical output at much lower cost. Real approach: caveman prompts for specific commands, fuller context when you need sophisticated or exploratory output.
Q: Doesn’t this contradict Anthropic’s prompting best practices?
Anthropic’s guidelines prioritize output quality through clear, comprehensive context. This method prioritizes cost efficiency for straightforward tasks. They’re complementary strategies, not contradictory, use best practices when quality and nuance matter most; use minimal prompts when you’re token-limited with clear, specific tasks.
Q: When should I actually try the caveman method?
If you’re hitting token limits (free tier burnout, API costs), this is worth testing immediately. For well-defined tasks where your goal is crystal clear, you’ll see real savings. Many users noted limits aren’t usually an issue, but if they are for you, minimal prompting offers tangible results.
Q: Isn’t token optimization just obvious stuff repackaged?
It is, token efficiency isn’t new. But the “caveman” framing with concrete before/after examples makes familiar advice click for people. Sometimes a memorable framework is exactly what makes developers actually try something they’ve intellectually known but never acted on.
i started talking to Claude like a caveman. my credits lasted 3x longer. i’m not joking.
by u/AdCold1610 in ChatGPTPromptGenius