llm-app-patterns

Name: llm-app-patterns
Availability: InStock
Author: involvex

Provide architectural patterns for building LLM applications with RAG pipelines and LLMOps monitoring.

Updated Mar 7, 2026

Search Tags:#architecture #prompt engineering #llm #rag #agents #llmops

Ready to run

npx skills add https://github.com/involvex/llms-remote --skill llm-app-patterns-involvex

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: llm-app-patterns
Source: https://github.com/involvex/llms-remote/tree/main/.agents/skills/llm-app-patterns
Command: npx skills add https://github.com/involvex/llms-remote --skill llm-app-patterns-involvex

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides battle-tested architectural patterns and code examples for building sophisticated LLM applications, from RAG pipelines to agentic systems 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 templates, versioning, chaining, and A/B testing.
LLMOps: Track key metrics, implement logging/tracing, and set up evaluation frameworks.
Use Case: Design a RAG system to answer questions based on your company's internal knowledge base, ensuring accurate and context-aware responses.

Quick Start

Use the llm-app-patterns skill to explore the ReAct agent pattern for building multi-step task execution.

Frequently Asked Questions about llm-app-patterns

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

FAQPage Schema

What are the best architectural patterns for building production-ready LLM applications?▼

Production-ready LLM applications rely on established architectural patterns like RAG pipelines, agent architectures, prompt engineering templates, and LLMOps monitoring to ensure scalable, robust AI systems.

How do I implement a RAG pipeline for answering questions from a company knowledge base?▼

Implementing a RAG pipeline involves document ingestion, embedding generation, retrieval strategies, and generation execution to provide accurate, context-aware responses from your internal knowledge base.

When should I choose the ReAct agent pattern over other agent architectures?▼

The ReAct agent pattern is ideal for multi-step task execution, whereas Plan-and-Execute or Multi-Agent architectures suit complex workflows requiring distinct planning, tool usage, and execution phases.

What metrics and frameworks are needed for setting up LLMOps monitoring?▼

LLMOps monitoring requires tracking key performance metrics, implementing logging and tracing, and setting up evaluation frameworks to measure LLM application reliability and output quality.

How do I manage prompt versioning and A/B testing in LLM apps?▼

Prompt engineering patterns utilize templates, versioning, chaining, and A/B testing frameworks to systematically manage, evaluate, and optimize prompt variations within LLM applications.