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AndyAnh174
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llm-app-patterns

Provide production-ready patterns and code examples for LLM applications.

Updated Jan 8, 2026
Search Tags:#prompt engineering#llm#rag#agents#llmops#application patterns
One-click install
npx skills add https://github.com/AndyAnh174/BanThuoc-SEO --skill llm-app-patterns-andyanh174
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-app-patterns
Source: https://github.com/AndyAnh174/BanThuoc-SEO/tree/main/.agent/skills/llm-app-patterns
Command: npx skills add https://github.com/AndyAnh174/BanThuoc-SEO --skill llm-app-patterns-andyanh174

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides battle-tested patterns and code examples for building robust, scalable, and observable LLM applications, addressing common challenges in RAG, agent design, and LLMOps.

Core Features & Use Cases

  • RAG Pipelines: Implement document ingestion, embedding, retrieval, and generation strategies.
  • Agent Architectures: Explore ReAct, Function Calling, Plan-and-Execute, and Multi-Agent patterns.
  • Prompt Engineering: Utilize prompt templates, versioning, and chaining for effective LLM interaction.
  • LLMOps: Integrate logging, tracing, caching, rate limiting, and evaluation frameworks.
  • Use Case: You're building a customer support chatbot that needs to answer questions based on your company's knowledge base. This Skill provides the RAG patterns to retrieve relevant information and generate accurate answers.

Quick Start

Use the llm-app-patterns skill to implement a RAG pipeline for your documents.

Frequently Asked Questions about llm-app-patterns

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I implement a RAG pipeline for ingesting documents and generating answers?▼

To implement a RAG pipeline, you need patterns for document ingestion, embedding, retrieval, and generation strategies. This Skill provides production-ready code examples covering these stages to help you build robust retrieval augmented generation applications.

What are the best agent architectures for building LLM applications?▼

Common LLM agent architectures include ReAct, Function Calling, Plan-and-Execute, and Multi-Agent patterns. This Skill provides battle-tested architectural patterns to help you design scalable and robust AI agents for various use cases.

How do I set up LLMOps monitoring for tracing and evaluating LLM applications?▼

Setting up LLMOps monitoring requires integrating logging, tracing, caching, rate limiting, and evaluation frameworks. This Skill provides production-ready patterns to establish comprehensive observability for your LLM applications.

What prompt engineering patterns should I use for effective LLM interaction?▼

Effective prompt engineering utilizes prompt templates, versioning, and chaining. This Skill provides code examples for these patterns to ensure consistent and manageable LLM interactions within your applications.

Can I use these patterns to build a customer support chatbot with a knowledge base?▼

Yes, you can use these RAG patterns to build a customer support chatbot that retrieves relevant information from your company's knowledge base and generates accurate answers to user questions.

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