ai-llm-engineering

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name: ai-llm-engineering
description: |
 Operational skill hub for LLM system architecture, evaluation, deployment, and optimization (modern production standards). Links to specialized skills for prompts, RAG, agents, and safety. Integrates recent advances: PEFT/LoRA fine-tuning, hybrid RAG handoff (see dedicated skill), vLLM 24x throughput, multi-layered security (90%+ bypass for single-layer), automated drift detection (18-second response), and CI/CD-aligned evaluation.
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# LLM Engineering – Operational Skill Hub

A single resource for executing, validating, and scaling LLM systems with **modern production standards**, while delegating domain depth to specialized skills.

This skill provides quick reference, decision frameworks, and navigation to detailed operational patterns for:

- Data, training, fine-tuning (PEFT/LoRA standard)
- Evaluation (automated testing, metrics, rollout gates)
- Deployment (vLLM 24x throughput, FP8/FP4 quantization)
- LLMOps (automated drift detection, retraining)
- Safety (multi-layered defenses, AI-powered guardrails)

**For detailed patterns:** See [Resources](#resources-best-practices--operational-patterns) and [Templates](#templates-copy-paste-ready) sections below.

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## Quick Reference

| Task | Tool/Framework | Command/Pattern | When to Use |
|------|----------------|-----------------|-------------|
| RAG Pipeline | LlamaIndex, LangChain | Page-level chunking + hybrid retrieval | Dynamic knowledge, 0.648 accuracy |
| Agentic Workflow | LangGraph, AutoGen, CrewAI | ReAct, multi-agent orchestration | Complex tasks, tool use required |
| Prompt Design | Anthropic, OpenAI guides | CoT, few-shot, structured | Task-specific behavior control |
| Evaluation | LangSmith, W&B, RAGAS | Multi-metric (hallucination, bias, cost) | Quality validation, A/B testing |
| Production Deploy | vLLM, TensorRT-LLM | FP8/FP4 quantization, 24x throughput | High-throughput serving, cost optimization |
| Monitoring | Arize Phoenix, LangFuse | Drift detection, 18-second response | Production LLM systems |

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## Decision Tree: LLM System Architecture

```text
Building LLM application: [Architecture Selection]
    ├─ Need current knowledge?
    │   ├─ Simple Q&A? → Basic RAG (page-level chunking + hybrid retrieval)
    │   └─ Complex retrieval? → Advanced RAG (reranking + contextual retrieval)
    │
    ├─ Need tool use / actions?
    │   ├─ Single task? → Simple agent (ReAct pattern)
    │   └─ Multi-step workflow? → Multi-agent (LangGraph, CrewAI)
    │
    ├─ Static behavior sufficient?
    │   ├─ Quick MVP? → Prompt engineering (CI/CD integrated)
    │   └─ Production quality? → Fine-tuning (PEFT/LoRA)
    │
    └─ Best results?
        └─ Hybrid (RAG + Fine-tuning + Agents) → Comprehensive solution
```

**See [Decision Matrices](resources/decision-matrices.md) for detailed selection criteria.**

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## When to Use This Skill

Claude should invoke this skill when the user asks about:

- LLM preflight/project checklists, production best practices, or data pipelines
- Building or deploying RAG, agentic, or prompt-based LLM apps
- Prompt design, chain-of-thought (CoT), ReAct, or template patterns
- Troubleshooting LLM hallucination, bias, retrieval issues, or production failures
- Evaluating LLMs: benchmarks, multi-metric eval, or rollout/monitoring
- LLMOps: deployment, rollback, scaling, resource optimization
- Technology stack selection (models, vector DBs, frameworks)
- Production deployment strategies and operational patterns

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## Scope Boundaries (Use These Skills for Depth)

- **Prompt design & CI/CD** → [ai-prompt-engineering](../ai-prompt-engineering/SKILL.md)
- **RAG pipelines & chunking** → [ai-llm-rag-engineering](../ai-llm-rag-engineering/SKILL.md)
- **Search tuning (BM25, HNSW, hybrid)** → [ai-llm-search-retrieval](../ai-llm-search-retrieval/SKILL.md)
- **Agent architectures & tools** → [ai-agents-development](../ai-agents-development/SKILL.md)
- **Serving optimization/quantization** → [