ai-llm-engineeringlisted

# ai-llm-engineering Use this skill for LLM integrations, agents, prompts, embeddings, retrieval, evaluation, model workflows, and AI pipelines. ## Principles Treat model output as untrusted and nondeterministic. Separate: - model instructions - application logic - tools - retrieval - persistence - evaluation Define expected outputs and failure behavior. For structured output use enforceable schemas where available. For tool use: - validate arguments - enforce authorization outside the model - limit tool capability - verify side effects For retrieval systems evaluate both retrieval quality and final answer quality. For prompts: - state the task clearly - provide relevant context - avoid irrelevant context - define output constraints where useful For evaluation use representative cases and frozen test sets when comparing changes. Do not tune against held-out evaluation cases. For expensive model workloads consider: - latency - token usage - caching - batching - retries - rate limits - fallback behavior Do not treat model confidence as proof of correctness. ## Adaptation Use project evidence to determine the actual language, framework, runtime, and existing conventions. When stack-specific implementation guidance is needed, prefer project evidence, native framework or language mechanisms, and relevant user-installed specialist skills. Technology-specific guidance must not redefine or weaken the core engineering requirement.