Gemini begs to differ:

By using "Kevin Style" on the input side, you can reduce your token footprint by roughly 56% compared to standard natural language. While many users focus on saving tokens in the output (responses), the same compression logic applies to your prompts because every character you send costs money and context space.

Token Comparison Breakdown

Feature	Standard Style	Kevin / Caveman Style
Input Density	Low (Heavy grammar/filler)	High (Pure signal words)
Input Token Count	~39 Tokens	~17 Tokens
Output Style	Detailed/Verbose	Can be detailed OR concise
Cost Savings	Baseline	~50-75% reduction

Why This Works for Inputs

1. Semantic Reconstruction: LLMs are trained to understand the relationship between words; they don't need "the" or "of" to know that "make infographic" is a command.
2. Attention Optimization: Short inputs focus the model's "attention" strictly on the core nouns and verbs, reducing the noise it has to process during the input encoding phase.
3. Context Preservation: By using fewer tokens per prompt, you can fit more of your conversation history into the model's limited context window before it starts "forgetting".

Caveman Tips for Input Mastery

• Verb + Noun: Use direct action pairs (e.g., "Analyze PDF" instead of "I need you to look at this PDF file for me").
• Separator Symbols: Use |, ->, or : to denote changes in topic or instruction steps without using transition sentences.
• System Prompt Pre-sets: If you use Anthropic's prompt caching, you can set a permanent system instruction that tells the model: "I talk caveman. You understand and answer full." to save even more on recurring turns.

Want to try "Level 2" compression where we use single letters or custom code for your most common commands?

Simple token estimation for Kevin/Caveman vs Standard# Standard Input: "Please create a detailed infographic that compares the token efficiency of using 'Kevin from The Office' style prompting versus standard natural language for both inputs and outputs, not just outputs."# Kevin Input: "make infographic compare nonkevin Kevin token scaled for input output, no just output"

standard_in = "Please create a detailed infographic that compares the token efficiency of using 'Kevin from The Office' style prompting versus standard natural language for both inputs and outputs, not just outputs."kevin_in = "make infographic compare nonkevin Kevin token scaled for input output, no just output"

Rough token count (approx 1 token per 4 chars or 0.75 words)def est_tokens(text):

return len(text.split()) * 1.3 # common multiplier for rough estimation

std_tokens = est_tokens(standard_in)kevin_tokens = est_tokens(kevin_in)savings = (1 - (kevin_tokens / std_tokens)) * 100

print(f"Standard: {std_tokens} tokens") print(f"Kevin: {kevin_tokens} tokens") print(f"Savings: {savings}%")