What’s inside

• A framework for choosing between prompting, RAG, LoRA/QLoRA, SFT, distillation, and DPO
• End-to-end LoRA and QLoRA fine-tuning on a single GPU
• Building a training-data pipeline with quality gates and lineage tracking
• Distilling smaller student models and aligning them with DPO
• Production ops: drift detection, canary prompts, rollback, and safety monitoring