Volume 3 — Model Lifecycle and Adaptation
How models are selected, modified, evaluated, compressed, deployed, and retired.
Reports
AI-ENG-G — Model Selection: Capability Fit, Deployment Fit & Failure Tolerance
Covers choosing models by task profile, reasoning depth, context length, tool use, modality, latency tolerance, cost ceiling, license constraints, language coverage, privacy requirements, hardware target, and acceptable failure modes. Teaches model choice as architecture, not leaderboard shopping.
AI-ENG-H — Model Adaptation: Fine-Tuning, LoRA, Preference Tuning & Distillation
Covers supervised fine-tuning, LoRA/QLoRA, preference tuning, domain adaptation, synthetic data generation, dataset design, adapter management, and distillation. Explains when adaptation improves behavior, when it merely overfits style, and when RAG or harness design would be cleaner.
AI-ENG-I — Regression Control: Model Registries, Rollouts & Behavioral Drift
Covers unified release custody for models, prompts, Skills, Augments, host adapters, tools, corpora, and workflows; lifecycle evidence; experiment tracking; canary and shadow testing; A/B tests; rollback; and silent regression detection. Behavior—not the most photogenic artifact—is the release.