ai-llm

name	ai-llm
description	Full LLM lifecycle skill — strategy selection, PEFT/LoRA, evaluation, and deployment. Use when building, fine-tuning, or operating LLM systems.

LLM Development & Engineering — Complete Reference

Build, evaluate, and deploy LLM systems with modern production standards.

This skill covers the full LLM lifecycle:

• Development: Strategy selection, dataset design, instruction tuning, PEFT/LoRA fine-tuning
• Evaluation: Automated testing, LLM-as-judge, metrics, rollout gates
• Deployment: Serving handoff, latency/cost budgeting, reliability patterns (see ai-llm-inference)
• Operations: Quality monitoring, change management, incident response (see ai-mlops)
• Safety: Threat modeling, data governance, layered mitigations (NIST AI RMF: https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf)

Modern Best Practices (2026):

• Treat the model as a component with contracts, budgets, and rollback plans (not "magic").
• Separate core concepts (tokenization, context, training vs adaptation) from implementation choices (providers, SDKs).
• Gate upgrades with repeatable evals and staged rollout; avoid blind model swaps.
• Cost-aware engineering: Measure cost per successful outcome, not just cost per token; design tiering/caching early.
• Security-by-design: Threat model prompt injection, data leakage, and tool abuse; treat guardrails as production code.

For detailed patterns: See Resources and Templates sections below.

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Quick Reference

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
Cost optimization	Tiered models + caching	Cascade routing, prompt caching, budget guardrails	Cost-sensitive production
Fine-tuning ROI	ROI calculator	Break-even analysis, TCO comparison	Investment decisions
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

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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.

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Cost-Quality Decision Framework

LLM spend is driven by usage-based inference (tokens/requests) plus supporting infra and engineering. Model selection is a cost-quality-latency-risk tradeoff.

Model Tier Strategy

| Tier | Typical profile | Use For | |------|--------|------|---------| | Value | Small/fast models | High-volume, simple tasks | | Balanced | General-purpose models | Most production workloads | | Premium | Frontier/large models | Hardest tasks, low volume |

Cost Optimization Levers

1. Model tiering: Route simple requests to cheaper models (often large savings at scale)
2. Prompt caching: Reuse stable prefixes/context (provider-specific discounts and constraints)
3. Prompt optimization: Compress examples and instructions (typically meaningful token reduction)
4. Output limits: Set appropriate max_tokens (prevents runaway costs)

When to Fine-Tune (ROI-Based)

Fine-tuning pays off when:

• Volume justifies it: >10k requests/month provides meaningful cost savings
• Domain is stable: Requirements unchanged for >6 months
• Data exists: >1,000 quality training examples available
• Break-even achievable: <12 months to recover investment

See Cost Economics for TCO modeling and Fine-Tuning ROI Calculator for investment analysis.

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Core Concepts (Vendor-Agnostic)

• Model classes: encoder-only, decoder-only, encoder-decoder, multimodal; choose based on task and latency.
• Tokenization & limits: context window, max output, and prompt/template overhead drive both cost and tail latency.
• Adaptation options: prompting → retrieval → adapters (LoRA) → full fine-tune; choose by stability and ROI (LoRA: https://arxiv.org/abs/2106.09685).
• Evaluation: metrics must map to user value; report uncertainty and slice performance, not only global averages.
• Governance: data retention, residency, licensing, and auditability are product requirements (EU AI Act: https://eur-lex.europa.eu/eli/reg/2024/1689/oj; NIST GenAI Profile: https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf).

Implementation Practices (Tooling Examples)

• Use a provider abstraction (gateway/router) to enable fallbacks and staged upgrades.
• Instrument requests with tokens, latency, and error classes (OpenTelemetry GenAI semantic conventions: https://opentelemetry.io/docs/specs/semconv/gen-ai/).
• Maintain prompt/model registries with versioning, changelogs, and rollback criteria.

Do / Avoid

Do

• Do pin model + prompt versions in production, and re-run evals before any change.
• Do enforce budgets at the boundary: max tokens, max tools, max retries, max cost.
• Do plan for degraded modes (smaller model, cached answers, “unable to answer”).

Avoid

• Avoid model sprawl (unowned variants with no eval coverage).
• Avoid blind upgrades based on anecdotal quality; require measured impact.
• Avoid training on production logs without consent, governance, and leakage controls.

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
• RAG pipelines & chunking → ai-rag
• Search tuning (BM25, HNSW, hybrid) → ai-rag
• Agent architectures & tools → ai-agents
• Serving optimization/quantization → ai-llm-inference
• Production deployment/monitoring → ai-mlops
• Security/guardrails → ai-mlops

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Resources (Best Practices & Operational Patterns)

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

Core Operational Patterns

• Cost Economics & Decision Frameworks - Cost modeling, unit economics, TCO analysis

  • Pricing/discount assumptions (verify against current provider docs)
  • Cost-quality tradeoff framework and decision matrix
  • Total Cost of Ownership (TCO) calculation
  • Fine-tuning ROI framework and break-even analysis
  • Prompt caching economics
  • Cost monitoring and budget guardrails

• 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 (tier-based, vendor-agnostic)
  • 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)
• RAG Best Practices - Quick reference (canonical skill: ai-rag)

Emerging Patterns

• Structured Output Patterns - JSON mode, constrained decoding, schema enforcement, validation pipelines
• Multimodal Patterns - Vision-language models, audio/image inputs, cross-modal pipelines, cost management
• Model Migration Guide - Provider migration playbook, eval-gated rollout, prompt adaptation, fallback strategies

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

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Templates (Copy-Paste Ready)

Production templates by use case and technology:

Selection & Governance

• Model Selection Matrix - Documented selection, scoring, licensing, and governance
• Fine-Tuning ROI Calculator - Investment analysis, break-even, go/no-go decisions

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

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Shared Utilities (Centralized patterns — extract, don't duplicate)

• ../software-clean-code-standard/utilities/llm-utilities.md — Token counting, streaming, cost estimation
• ../software-clean-code-standard/utilities/error-handling.md — Effect Result types, correlation IDs
• ../software-clean-code-standard/utilities/resilience-utilities.md — p-retry v6, circuit breaker for LLM API calls
• ../software-clean-code-standard/utilities/logging-utilities.md — pino v9 + OpenTelemetry integration
• ../software-clean-code-standard/utilities/observability-utilities.md — OpenTelemetry SDK, tracing, metrics
• ../software-clean-code-standard/utilities/config-validation.md — Zod 3.24+, secrets management for API keys
• ../software-clean-code-standard/utilities/testing-utilities.md — Test factories, fixtures, mocks
• ../software-clean-code-standard/references/clean-code-standard.md — Canonical clean code rules (CC-*) for citation

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Trend Awareness Protocol

IMPORTANT: For “best/latest” recommendations, verify recency using current sources (official docs/release notes/benchmarks). If you can’t browse, state assumptions and ask for timeframe + constraints.

Trigger Conditions

• "What's the best LLM model for [use case]?"
• "What should I use for [RAG/fine-tuning/agents]?"
• "What's the latest in LLM development?"
• "Current best practices for [prompting/evaluation/deployment]?"
• "Is [model/framework] still relevant in 2026?"
• "[Model A] vs [Model B]?" or "[Framework A] vs [Framework B]?"
• "Best vector database for [use case]?"
• "What agent framework should I use?"

Minimal Verification Checklist

1. Confirm user constraints: latency, cost, privacy/compliance, deployment target, and toolchain.
2. Check at least 2 authoritative sources from data/sources.json (provider docs, release notes, pricing/quotas, deprecations).
3. Prefer stable guidance (tradeoffs + decision criteria) over “one best model/framework”.

What to Report

After searching, provide:

• Current landscape: What models/frameworks are popular NOW (not 6 months ago)
• Emerging trends: New models, frameworks, or techniques gaining traction
• Deprecated/declining: Models/frameworks losing relevance or support
• Recommendation: Based on fresh data, not just static knowledge

Example Topics (verify with fresh sources)

• Latest frontier models (GPT-4.5, Claude 4, Gemini 2.x, Llama 4)
• Agent frameworks (LangGraph, CrewAI, AutoGen, Semantic Kernel)
• Vector databases (Pinecone, Qdrant, Weaviate, pgvector)
• RAG techniques (contextual retrieval, agentic RAG, graph RAG)
• Inference engines (vLLM, TensorRT-LLM, SGLang)
• Evaluation frameworks (RAGAS, DeepEval, Braintrust)

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Related Skills

This skill integrates with complementary Claude Code skills:

Core Dependencies

• ai-rag - Retrieval pipelines: chunking, hybrid search, reranking, evaluation
• ai-prompt-engineering - Systematic prompt design, evaluation, testing, and optimization
• ai-agents - Agent architectures, tool use, multi-agent systems, autonomous workflows

Production & Operations

• ai-llm-inference - Production serving, quantization, batching, GPU optimization
• ai-mlops - Deployment, monitoring, incident response, security, and governance

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External Resources

See data/sources.json for 50+ curated authoritative sources:

• Official LLM platform docs - OpenAI, Anthropic, Gemini, Mistral, Azure OpenAI, AWS Bedrock
• Open-source models and frameworks - HuggingFace Transformers, open-weight models, PEFT/LoRA, distributed training/inference stacks
• RAG frameworks and vector DBs - LlamaIndex, LangChain 1.2+, LangGraph, LangGraph Studio v2, Haystack, Pinecone, Qdrant, Chroma
• Agent frameworks (examples) - LangGraph, Semantic Kernel, AutoGen, CrewAI
• RAG innovations (examples) - Graph-based retrieval, hybrid retrieval, online evaluation loops
• Prompt engineering - Anthropic Prompt Library, Prompt Engineering Guide, CoT/ReAct patterns
• Evaluation and monitoring - OpenAI Evals, HELM, Anthropic Evals, LangSmith, W&B, Arize Phoenix
• Production deployment - Model gateways/routers, self-hosted serving, managed endpoints

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Usage

For New Projects

1. Start with Production Checklists - Validate all pre-deployment requirements
2. Use Decision Matrices - Select technology stack
3. Reference Project Planning Patterns - Design FTI pipeline
4. Implement with Common Design Patterns - Copy-paste code examples
5. Avoid Anti-Patterns - Learn from common mistakes

For Troubleshooting

1. Check Anti-Patterns - Identify failure modes and mitigations
2. Use Decision Matrices - Evaluate if architecture fits use case
3. Reference Common Design Patterns - Verify implementation correctness

For Ongoing Operations

1. Follow Production Checklists - Weekly operational tasks
2. Integrate Evaluation Patterns - Continuous quality monitoring
3. Apply LLMOps Best Practices - Deployment and rollback procedures

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Navigation Summary

Quick Decisions: Decision Matrices Pre-Deployment: Production Checklists Planning: Project Planning Patterns Implementation: Common Design Patterns Troubleshooting: Anti-Patterns

Domain Depth: LLMOps | Evaluation | Prompts | Agents | RAG

Templates: assets/ - Copy-paste ready production code

Sources: data/sources.json - Authoritative documentation links

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Fact-Checking

• Use web search/web fetch to verify current external facts, versions, pricing, deadlines, regulations, or platform behavior before final answers.
• Prefer primary sources; report source links and dates for volatile information.
• If web access is unavailable, state the limitation and mark guidance as unverified.