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.