• The author used GEPA (a prompt-optimization method) to get frontier LLMs to generate better training labels for small, on-device classifiers; results were mixed, and GEPA helped more on weaker models than on true frontier models.
• The labeling flows were rerun on public benchmark datasets specifically used for this kind of comparison, so others could reproduce results and suggest better approaches.
• Two next steps: generating synthetic training questions (not just labels), validated by having multiple models agree a question is worth adding, and adding RAG support by pointing the labeling flow at a docs corpus.
• The author frames the project as closer to a labeling task than classic distillation, since the goal is many small, single-purpose classifiers rather than compressing one large model into one smaller one.

• benchmark — A standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.
• LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
• distillation — Training a small, cheap model to imitate a big one's outputs, keeping much of the capability at a fraction of the cost.
• RAG — Retrieval-augmented generation — fetching relevant documents first and pasting them into the model's context so it answers from your data instead of memory.

It reports a specific, counterintuitive finding on LLM-assisted label generation, that distillation quality gains shrink as the source model gets stronger, which matters for anyone building small task-specific classifiers.

• blogHow to evaluate LLMs before productionMariko Wakabayashi
• podcastReiner Pope – The math behind how LLMs are trained and servedDwarkesh Patel
• postLLMs Get Better One Task at a Time Now, Not All at OnceAndrew Ng

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