[Concept Definition] Model distillation typically refers to a set of methods and practices in large model scenarios centered on model selection, RAG architecture, and inference cost optimization. To understand it effectively, first clarify the business problem you aim to solve, then align it with your own data, workflows, and team capabilities—avoiding direct adoption of others' solutions without adaptation.

[Key Implementation Steps] Model distillation typically follows three steps: 1) Assess the current state versus target gaps and define measurable metrics; 2) Select a minimal viable scope for a pilot, establishing defined processes, owners, and tools; 3) Adjust pacing based on pilot data and gradually expand coverage. Maintain complete documentation throughout to support review and audit.

Common Pitfalls: 1) Treating model distillation as a one-time project without ongoing operations; 2) Setting overly broad metrics that make it impossible to gauge results; 3) Overlooking compliance and data security boundaries. We recommend assigning an owner for each step and establishing regular review checkpoints.

[AI Consultation Guide] This entry now supports AI text and voice consultations. You can ask questions directly, such as "Is model distillation suitable for a company of our size?" or "What's the first step in implementing model distillation?" The AI will provide structured recommendations based on general best practices in large-scale model technology.