Student builds LLM router using Bloom's taxonomy for code

A computer science undergraduate is developing an LLM router for code and codebases, focusing on token economics. Instead of relying on heavily fine-tuned LLMs for routing, the student is measuring prompt complexity by analyzing the interaction of signals, including a novel metric called 'blooms_intent' derived from Bloom's taxonomy. The student is seeking advice on suitable datasets, the efficacy of using AI for dataset bootstrapping, and optimal dataset sizes and classifiers for differentiating query nuances. AI

IMPACT This project explores novel methods for LLM routing, potentially improving efficiency and cost-effectiveness in code-related AI applications.

RANK_REASON The cluster describes a student's project building a tool, not a major industry release or research breakthrough.

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• Bloom's taxonomy
• LLM

• product
• other

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