Show HN: Shrewd – what I learned distilling LLM labels into local classifiers (github.com)

A developer has introduced "Shrewd," a local classifier aimed at leveraging large language model (LLM) insights to optimize text classification tasks efficiently. By applying GEPA prompt optimization, Shrewd distills LLM-based labels into a smaller, faster model that can operate offline on individual tasks, effectively addressing concerns around inference costs, latency, and data privacy. Users can choose various classifier types, such as basic classifiers for single-label predictions or decision panels handling multiple questions, thus catering to a wide range of classification needs. This development is significant for the AI/ML community because it circumvents the reliance on continuous LLM access by enabling localized training models that utilize LLM output as authoritative labels. Key findings from initial tests indicate that while prompt optimization can enhance label quality, the improvements may not always translate to better student performance, emphasizing the importance of careful model selection and dataset management. The Shrewd repository includes tools for prompt optimization, teacher-student interactions, and several prebuilt classifiers, making it accessible for practitioners looking to implement efficient local AI solutions.