Discover a groundbreaking approach where advanced AI models evaluate the performance of other AIs. This system benchmarks models against human intelligence, scoring them from 'uneducated' to 'world-class.' Innovative techniques push for deeper evaluations, creating a feedback loop to enhance future outputs. The methodology remains relevant, even with newer AI advancements. It's an intriguing exploration of how to measure and improve AI capabilities through a human-centric lens.
09:35
forum Ask episode
web_stories AI Snips
view_agenda Chapters
auto_awesome Transcript
info_circle Episode notes
question_answer ANECDOTE
Smart AI Rating Other AIs
Daniel Miessler built a system where a smarter AI rates the output of a less smart AI on tasks.
This system scores AIs on a human-level scale from uneducated up to world-class and superhuman level.
insights INSIGHT
AI Scores Reflect Intelligence Levels
Stronger AI models score higher on a human-equivalent intelligence scale than weaker models.
The system successfully differentiates intelligence levels like high school to PhD or world-class human.
volunteer_activism ADVICE
Craft Deep Evaluation Prompts
Use detailed prompts instructing the evaluating AI to deeply analyze input, instructions, and output.
Ask the AI to rate performance across many nuanced dimensions and suggest improvements for higher scores.
Get the Snipd Podcast app to discover more snips from this episode
In this episode, I walk through a Fabric Pattern that assesses how well a given model does on a task relative to humans. This system uses your smartest AI model to evaluate the performance of other AIs—by scoring them across a range of tasks and comparing them to human intelligence levels.
I talk about:
1. Using One AI to Evaluate Another The core idea is simple: use your most capable model (like Claude 3 Opus or GPT-4) to judge the outputs of another model (like GPT-3.5 or Haiku) against a task and input. This gives you a way to benchmark quality without manual review.
2. A Human-Centric Grading System Models are scored on a human scale—from “uneducated” and “high school” up to “PhD” and “world-class human.” Stronger models consistently rate higher, while weaker ones rank lower—just as expected.
3. Custom Prompts That Push for Deeper Evaluation The rating prompt includes instructions to emulate a 16,000+ dimensional scoring system, using expert-level heuristics and attention to nuance. The system also asks the evaluator to describe what would have been required to score higher, making this a meta-feedback loop for improving future performance.
Note: This episode was recorded a few months ago, so the AI models mentioned may not be the latest—but the framework and methodology still work perfectly with current models.
Unsupervised Learning