Llm app patterns

LLM Application Patterns

Expert in production LLM application patterns and architectures.

When to Use This Skill

Use when:

• Building production RAG (Retrieval-Augmented Generation) pipelines
• Implementing AI agents with tool use and multi-step reasoning
• Designing prompt engineering strategies and template systems
• Setting up LLMOps: monitoring, logging, tracing, and evaluation
• Deploying LLM applications with caching, rate limiting, and fallbacks
• Choosing between different agent architectures (ReAct, function calling, plan-execute, multi-agent)
• Optimizing retrieval: chunking strategies, vector databases, hybrid search
• Building production-ready systems: cost optimization, reliability, observability

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Core Capabilities

This skill provides production-proven patterns for:

1. RAG Pipelines - Document ingestion, chunking, embedding, retrieval, generation
2. Agent Architectures - ReAct, function calling, plan-execute, multi-agent collaboration
3. Prompt Engineering - Templates, versioning, A/B testing, chaining
4. LLMOps & Monitoring - Metrics, logging, tracing, evaluation frameworks
5. Production Patterns - Caching, rate limiting, retry logic, fallbacks

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Pattern References

For detailed implementation guidance, see:

RAG Pipelines

Use when: Building search-augmented LLM applications

Covers:

• Document ingestion and preprocessing
• Chunking strategies (fixed, semantic, sliding window)
• Vector database selection and configuration
• Retrieval patterns (dense, sparse, hybrid, multi-vector)
• Generation with retrieved context

Agent Architectures

Use when: Building agents that use tools or multi-step reasoning

Covers:

• ReAct pattern (Reasoning + Acting)
• Function calling for structured tool use
• Plan-and-execute for complex tasks
• Multi-agent collaboration patterns
• Architecture decision matrix

Prompt Engineering

Use when: Creating reusable prompt systems

Covers:

• Prompt templates with variables
• Versioning and A/B testing
• Prompt chaining for multi-step workflows
• Few-shot learning patterns
• Best practices for prompt structure

LLMOps & Observability

Use when: Setting up monitoring and evaluation

Covers:

• Key metrics to track (performance, quality, cost, reliability)
• Logging and distributed tracing
• Evaluation frameworks and benchmarking
• Caching strategies for cost reduction
• Rate limiting and retry patterns
• Fallback strategies for reliability

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Quick Decision Guide

Goal	Reference
Answer questions from your docs	RAG Pipelines
Build tool-using agent	Agent Architectures
Create reusable prompts	Prompt Engineering
Monitor production system	LLMOps & Observability

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Dependencies

• architect - For overall system design and architecture decisions
• data-modeler - For data schema design in RAG pipelines
• ops-manager - For production deployment and operations