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 and Templates sections below.

Quick Reference

Decision Tree: LLM System Architecture

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 for detailed selection criteria.

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

Scope Boundaries (Use These Skills for Depth)

• Prompt design & CI/CD → ai-prompt-engineering
• RAG pipelines & chunking → ai-llm-rag-engineering
• Search tuning (BM25, HNSW, hybrid) → ai-llm-search-retrieval
• Agent architectures & tools → ai-agents-development
• Serving optimization/quantization → ai-llm-ops-inference
• Production deployment/monitoring → ai-ml-ops-production
• Security/guardrails → ai-ml-ops-security

Resources (Best Practices & Operational Patterns)

Comprehensive operational guides with checklists, patterns, and decision frameworks:

Core Operational Patterns

• Project Planning Patterns - Stack selection, FTI pipeline, performance budgeting

  • AI engineering stack selection matrix
  • Feature/Training/Inference (FTI) pipeline blueprint
  • Performance budgeting and goodput gates
  • Progressive complexity (prompt → RAG → fine-tune → hybrid)

• Production Checklists - Pre-deployment validation and operational checklists

  • LLM lifecycle checklist (modern production standards)
  • Data & training, RAG pipeline, deployment & serving
  • Safety/guardrails, evaluation, agentic systems
  • Reliability & data infrastructure (DDIA-grade)
  • Weekly production tasks

• Common Design Patterns - Copy-paste ready implementation examples

  • Chain-of-Thought (CoT) prompting
  • ReAct (Reason + Act) pattern
  • RAG pipeline (minimal to advanced)
  • Agentic planning loop
  • Self-reflection and multi-agent collaboration

• Decision Matrices - Quick reference tables for selection

  • RAG type decision matrix (naive → advanced → modular)
  • Production evaluation table with targets and actions
  • Model selection matrix (GPT-4, Claude, Gemini, self-hosted)
  • Vector database, embedding model, framework selection
  • Deployment strategy matrix

• Anti-Patterns - Common mistakes and prevention strategies

  • Data leakage, prompt dilution, RAG context overload
  • Agentic runaway, over-engineering, ignoring evaluation
  • Hard-coded prompts, missing observability
  • Detection methods and prevention code examples

Domain-Specific Patterns

• LLMOps Best Practices - Operational lifecycle and deployment patterns
• Evaluation Patterns - Testing, metrics, and quality validation
• Prompt Engineering Patterns - Quick reference (canonical skill: ai-prompt-engineering)
• Agentic Patterns - Quick reference (canonical skill: ai-agents-development)
• RAG Best Practices - Quick reference (canonical skill: ai-llm-rag-engineering)

Note: Each resource file includes preflight/validation checklists, copy-paste reference tables, inline templates, anti-patterns, and decision matrices.

Templates (Copy-Paste Ready)

Production templates by use case and technology:

RAG Pipelines

• Basic RAG - Simple retrieval-augmented generation
• Advanced RAG - Hybrid retrieval, reranking, contextual embeddings

Prompt Engineering

• Chain-of-Thought - Step-by-step reasoning pattern
• ReAct - Reason + Act for tool use

Agentic Workflows

• Reflection Agent - Self-critique and improvement
• Multi-Agent - Manager-worker orchestration

Data Pipelines

• Data Quality - Validation, deduplication, PII detection

Deployment

• LLM Deployment - Production deployment with monitoring

Evaluation

• Multi-Metric Evaluation - Comprehensive testing suite

Related Skills

This skill integrates with complementary Claude Code skills:

Core Dependencies

• ai-llm-rag-engineering - Advanced RAG patterns, chunking strategies, hybrid retrieval, reranking
• ai-llm-search-retrieval - Search optimi