LinkMark KashefTutorial
Teams often default to complex cloud LLMs when compact, specialized models running locally can handle structured decision tasks faster, cheaper, and with higher determinism. This resource demonstrates how to enforce baseline metrics and disciplined evaluation before jumping into training.
Kashef highlights best practices in applied ML engineering by prioritizing baseline benchmarks over immediate fine-tuning. The explicit 'can't tell' label prevents forced false classifications when evidence is lacking in booking policies. Additionally, using a strict eight-part prompt framework guides assistants like Claude to operate locally, keep held-out evaluation sets clean, and log every failure case transparently.
Summary generated by AI (Gemini 3.8 Flash, 28 September 2026). It may contain errors – the original source is authoritative.
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