Unsupervised Learning

Using the Smartest AI to Rate Other AI

Apr 19, 2025

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

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Quick takeaways

The podcast introduces a methodology that uses advanced AI models to evaluate the performance of other AIs against human intelligence benchmarks.

A comprehensive scoring system enables nuanced feedback on AI outputs, facilitating continuous improvement and higher standards in AI technology assessments.

Deep dives

Evaluating AI Performance through Meta Assessment

A system has been developed to evaluate one AI by using a more advanced AI as a judge. This involves inputting a piece of content, such as a blog post, along with specific instructions for the AI being tested. The primary AI assesses the output based on a detailed scoring system that includes a range of educational levels, from high school to superhuman. The evaluation aims to classify the performance of the tested AI by comparing it to a spectrum of human capabilities, while also revealing how different AI models score against one another, reflecting their respective competencies.

Evaluating AI Performance Through Advanced Comparison Systems

7min

Enhancing AI Evaluation Through Innovative Techniques

2min

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.

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