Can I use this for multiple AI providers?

Absolutely. It provides standardized implementation patterns and code snippets for both Anthropic and OpenAI SDKs.

What is the LLM Application Development skill?

It is a specialized capability for Claude Code that provides patterns and best practices for building robust, production-grade AI-driven software.

How does it handle API errors like rate limits?

The skill includes safe wrapper functions that implement exponential backoff, retry logic, and fallback mechanisms to ensure application stability.

Does this skill support RAG implementation?

Yes, it includes detailed patterns for document chunking, embedding storage using tools like pgvector, and context-aware retrieval.

Is this skill suitable for production use?

Yes, it focuses on production concerns such as token management, cost control, response streaming, and output validation guardrails.

LLM Application Development

Name: LLM Application Development
Author: plurigrid

byplurigrid

•

数据科学与机器学习

Streamlines the creation of AI-powered features through expert prompt engineering, RAG patterns, and multi-model API integrations.

关于

This skill provides a comprehensive framework for building production-ready LLM applications, offering Claude specific guidance on prompt engineering techniques like Chain of Thought and few-shot learning. It equips developers with standardized implementation patterns for Retrieval-Augmented Generation (RAG), efficient document chunking, and robust integration with leading AI providers like OpenAI and Anthropic. Designed to bridge the gap between simple chat interfaces and complex AI-driven systems, it includes best practices for error handling, token management, and cost-effective scaling within TypeScript and JavaScript environments.

主要功能

Best practice guidance for token management, caching, and output guardrails
Advanced prompt engineering templates for structured and Chain of Thought reasoning
End-to-end RAG pipeline patterns including vector database storage and document chunking
Robust error handling strategies with exponential backoff and retries
Standardized API integration for OpenAI and Anthropic with streaming support
2 GitHub stars

使用场景

Building context-aware AI chatbots using RAG and vector databases
Integrating multi-model LLM workflows into existing web applications
Developing automated content classification and sentiment analysis systems

关于

主要功能

Best practice guidance for token management, caching, and output guardrails
Advanced prompt engineering templates for structured and Chain of Thought reasoning
End-to-end RAG pipeline patterns including vector database storage and document chunking
Robust error handling strategies with exponential backoff and retries
Standardized API integration for OpenAI and Anthropic with streaming support
2 GitHub stars

使用场景

Building context-aware AI chatbots using RAG and vector databases
Integrating multi-model LLM workflows into existing web applications
Developing automated content classification and sentiment analysis systems