ai-llm

# LLM Engineering - Lifecycle Skill **Modern Best Practices**: treat the model as a versioned component with contracts, eval gates, rollout controls, cost budgets, and explicit fallback paths. Prefer stable decision criteria over static "best model" lists, and verify volatile provider facts against current official docs before recommending a stack. This skill is the **umbrella skill** for deciding how to build, adapt, evaluate, migrate, and operate LLM systems. Use this skill for **architecture and lifecycle decisions**. Use sibling skills for **implementation depth**. No theory. No generic AI history. Focus on operational choices, tradeoffs, checklists, and reusable templates. ## ASCII Flow ```text LLM product need | v outcome contract task + users + quality + latency + cost + privacy/compliance | v architecture choice prompt-only -> RAG -> tools/agents -> adaptation/fine-tuning -> hybrid | v evaluation and rollout golden set + edge cases + canary + observability + rollback | v operated model component versioned prompts/models/configs + fallbacks + governance ``` ## When to Use This Skill Activate this skill when the user asks for: - Choosing between prompt-only, RAG, tool use, fine-tuning, or hybrid LLM architectures - Selecting a provider, model tier, or deployment path for a production workload - Planning or reviewing model/provider migrations - Designing eval suites, graders, rollout gates, and regression policies - Calculating cost, late...