ai-llm-engineeringlisted

# 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. --- ## 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,