| name | ai-llm-engineering |
| description | Design, integrate, evaluate, and operate AI/LLM systems using model- and framework-agnostic engineering principles. |
| metadata | {"internal":true} |
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.
