Transfer learning involves using a pre-trained model on a new, related task to leverage existing knowledge and reduce training time. This approach allows us to build upon existing AI capabilities rather than starting from scratch.

NYD Application: We use transfer learning to adapt general coding models for our specific tech stack (Lovable, Supabase, etc.) without training from zero.

Example: "We used transfer learning to adapt a general code completion model for our specific React component library."