llm-app-patterns

Name: llm-app-patterns
Availability: InStock
Author: Yog-Sotho

Implement reusable patterns for RAG pipelines, agent architectures, and LLMOps.

4|Updated Mar 21, 2026

Search Tags:#llm #rag #agent #patterns #observability #prompt-engineering #llmops

Ready to run

npx skills add https://github.com/Yog-Sotho/claude-skills --skill llm-app-patterns-yog-sotho

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: llm-app-patterns
Source: https://github.com/Yog-Sotho/claude-skills/tree/main/llm-app-patterns
Command: npx skills add https://github.com/Yog-Sotho/claude-skills --skill llm-app-patterns-yog-sotho

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Building robust, scalable LLM-powered applications is complex and time-consuming without reusable patterns.

Core Features & Use Cases

RAG pipelines, multi-agent architectures, and prompt engineering patterns for reliable results.
Caching, observability, rate limiting, and LLMOps integration to monitor cost, latency, and reliability.
Use cases include building production-grade chat assistants, document QA bots, and enterprise AI assistants with governance.

Quick Start

Describe your LLM app goals and apply the core patterns to scaffold your project.

Frequently Asked Questions about llm-app-patterns

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

FAQPage Schema

What are production-ready patterns for LLM apps?▼

Production-ready patterns for LLM apps are reusable architectures for RAG pipelines, multi-agent systems, and prompt engineering that ensure reliable results. They include caching, observability, and LLMOps to monitor cost, latency, and reliability.

How do I build a reliable RAG pipeline for an enterprise AI assistant?▼

To build a reliable RAG pipeline for an enterprise AI assistant, apply reusable patterns for prompt engineering, caching, and observability. This ensures scalable, monitored document QA bots with proper governance and reliable results.

Do I need Python and DevOps knowledge to implement LLMOps?▼

Yes, you need Python, AI tooling knowledge, and DevOps practices to implement LLMOps and scalable LLM applications. These prerequisites enable you to integrate caching, rate limiting, and observability to monitor cost and reliability.

What's the best way to monitor cost and latency in LLM applications?▼

The best way to monitor cost and latency in LLM applications is by applying LLMOps patterns with integrated observability and caching. This approach monitors reliability and rate limiting across real-world tasks for production-grade systems.

Can I use these LLM patterns for multi-agent architectures?▼

Yes, you can use these LLM patterns for multi-agent architectures. They specifically address building reliable, production-grade LLM applications with reusable patterns for agent architectures, prompt engineering, and observability.

When should I not use complex patterns for my LLM app?▼

You should avoid complex patterns for your LLM app if your project lacks Python, AI tooling knowledge, and DevOps practices. These patterns target scalable, monitored, production-grade systems requiring governance and LLMOps integration.