Routing LLMs by task verifiability: a small experiment (n=120, 3 models) inspired by Karpathy's framework [D]
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Learn how to route LLMs by task verifiability using Karpathy's framework and improve model performance on high-verifiability tasks
Action Steps
- Apply Karpathy's framework to classify tasks by verifiability
- Run experiments to compare model performance on high and low verifiability tasks
- Configure LLMs to prioritize high-verifiability tasks
- Test the impact of task verifiability on model accuracy and error rates
- Compare results across different LLM models and task types
Who Needs to Know This
ML engineers and researchers can benefit from this approach to optimize LLM performance and reduce errors, while product managers can use this to inform product development and prioritize high-verifiability tasks
Key Insight
💡 High-verifiability tasks can be mechanically checked, reducing errors and improving model performance
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🤖 Improve LLM performance by routing tasks by verifiability! 📈
Key Takeaways
Learn how to route LLMs by task verifiability using Karpathy's framework and improve model performance on high-verifiability tasks
Full Article
Full disclosure: this is directional, not a paper. n=120 tasks, one internal evaluator, not peer reviewed. I work at an LLM infrastructure company. This experiment was done on my own time and is not a company claim. Karpathy's framework classifies tasks by verifiability. Can output be mechanically checked? High verifiability tasks like code compilation and structured JSON extraction are safer because the verifier catches errors. Low verifiability tasks li
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