Single-shot · temp 0.7 · real votes · identical prompts·How we test →
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
Explain how a large language model (like GPT or Claude) learns and generates text to each of these three audiences: 1. **An experienced software engineer** who has built distributed systems and APIs but has never touched machine learning. They're skeptical that "predicting the next word" can produce intelligent behavior. 2. **A PhD physicist** who thinks AI is overhyped and wants to understand what's actually novel vs. what's just well-marketed linear algebra. They expect mathematical precision. 3. **A venture capitalist** evaluating an AI startup. They need to understand the technology well enough to assess defensibility, moats, and whether the founders' claims are credible. Each explanation should be 2-3 paragraphs, tailored to their specific background, vocabulary, concerns, and what they'd find most compelling or interesting. Don't just change the difficulty level. Change the framing, examples, and emphasis for each audience.