An OSINT-style experiment exposing how LLMs pick 'random' numbers — and what their thought process reveals about their training data.

You've seen the trend. Someone asks an AI: "Pick a random number between 1 and 100."

It says 73 . Or 42 . Every time.

Funny meme, right? Wrong. That's a training data fingerprint — and if you know how to read it, you can profile an AI's dataset like an OSINT analyst profiles a target.

I ran the experiment properly. 6 models. 3 different prompts. Documented every response — including the thought process.

Here's what I found.

The Setup

Three rounds, same 6 models:

Model Who built it

Claude Sonnet 4.6 Anthropic

Gemini Pro Google

Copilot Microsoft / OpenAI

DeepSeek DeepSeek AI

GLM-5.1 Zhipu AI

Grok xAI

Round 1 — Neutral prompt:

Pick a random number between 1 and 100.

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Round 2 — Developer context:

I'm a backend developer testing an RNG function.…