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Production LLM engineering skill. Covers strategy selection (prompting vs RAG vs fine-tuning), dataset design, PEFT/LoRA, evaluation workflows, deployment handoff to inference serving, and lifecycle operations with cost/safety controls.
Passed 16 of 22 test cases with the skill, and 16 of 22 without it.
0 fixed16 passed either way6 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
The instructions your model reads when the skill loads.
Build, evaluate, and deploy LLM systems with modern production standards.
This skill covers the full LLM lifecycle:
ai-llm-inference)ai-mlops)Modern Best Practices (January 2026):
For detailed patterns: See Resources and Templates sections below.
| Task | Tool/Framework | Command/Pattern | When to Use |
|---|---|---|---|
| Choose architecture | Prompt vs RAG vs fine-tune | Start simple; add retrieval/adaptation only if needed | New products and migrations |
| Model selection | Scoring matrix | Quality/latency/cost/privacy/license weighting | Provider changes and procurement |
| Prompt contracts | Structured output + constraints | JSON schema, max tokens, refusal rules | Reliability and integration |
| RAG integration | Hybrid retrieval + grounding | Retrieve → rerank → pack → cite → verify | Fresh/large corpora, traceability |
| Fine-tuning | PEFT/LoRA (when justified) | Small targeted datasets + regression suite | Stable domains, repeated tasks |
| Evaluation | Offline + online | Golden sets + A/B + canary + monitoring | Prevent regressions and drift |
Building LLM application: [Architecture Selection]
├─ Need current knowledge?
test cases passed, vs 16 / 22 without
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