Llm app patterns
Skill karim-bhalwani/agent-skills-collection/skills/llm-app-patterns
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Production LLM application patterns, architectures, and best practices. Covers RAG pipelines, agent architectures, prompt engineering, LLMOps, and production deployment patterns.
SKILL.md
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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
Core Capabilities
This skill provides production-proven patterns for:
- RAG Pipelines - Document ingestion, chunking, embedding, retrieval, generation
- Agent Architectures - ReAct, function calling, plan-execute, multi-agent collaboration
- Prompt Engineering - Templates, versioning, A/B testing, chaining
- LLMOps & Monitoring - Metrics, logging, tracing, evaluation frameworks
- Production Patterns - Caching, rate limiting, retry logic, fallbacks
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
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 |
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
Gives 0 of the 12 instructions most prompt engineering skills give in 704 tokens
Counted across 563 of the 626 authors here whose files we hold, read 2026-08-06
- ask at most three clarifying questionsin 22 of 563, across 15 files
- respond in the user input languagein 14 of 563, across 9 files
- preserve the original intentin 13 of 563, across 11 files
- Establish baseline metrics and collect representative examplesin 12 of 563, across 2 files
- Identify failure modes and prioritize high-impact fixesin 12 of 563, across 2 files
- Apply prompt and workflow improvements with measurable goalsin 12 of 563, across 2 files
- Roll back quickly if quality or safety metrics regressin 12 of 563, across 2 files
- validate changes with tests and roll out in controlled stagesin 12 of 563, across 2 files
- generate quantitative baseline performance reportsin 12 of 563, across 2 files
- create representative test scenariosin 12 of 563, across 2 files
- treat prompts as codein 12 of 563, across 5 files
- test prompts on diverse inputsin 12 of 563, across 8 files
Said here and by no other author read
- build production rag pipelines
- implement agents with tool use
- design prompt engineering strategies
- set up llmops monitoring and evaluation
- deploy with caching and fallbacks
- choose appropriate agent architectures
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.