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llm-application-dev

Build LLM applications with prompt engineering, RAG pipelines, and OpenAI/Anthropic API integrations.

2|Updated Jan 27, 2026
Search Tags:#prompt engineering#API integration#RAG#chatbots#large language models#AI applications
One-click install
npx skills add https://github.com/novskidev/codex-skill-template --skill llm-application-dev-novskidev
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-application-dev
Source: https://github.com/novskidev/codex-skill-template/tree/main/skills/llm-application-dev
Command: npx skills add https://github.com/novskidev/codex-skill-template --skill llm-application-dev-novskidev

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires openai, anthropic, pgvector, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies the development of applications using Large Language Models (LLMs), covering prompt engineering, Retrieval-Augmented Generation (RAG) patterns, and LLM integration, making AI-powered features, chatbots, and LLM-based automation achievable.

Core Features & Use Cases

  • Prompt Engineering: Tools for structured prompts, few-shot examples, and chain of thought processes.
  • API Integration: Patterns for OpenAI and Anthropic models, with streaming response capabilities.
  • RAG (Retrieval-Augmented Generation): Basic RAG pipeline with document chunking and embedding storage.
  • Error Handling: Implementing retry logic and fallbacks for robust error handling.
  • Best Practices: Guidance on token management, caching, evaluation, and privacy.
  • Use Case: Create a chatbot that can answer technical questions using LLMs by integrating and customizing the Skill's provided patterns.

Quick Start

Use the llm-application-dev skill to create a chatbot that can explain complex code concepts in simple language.

Frequently Asked Questions about llm-application-dev

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

FAQPage Schema
How do I build a chatbot with Large Language Models like OpenAI and Anthropic?▼

To build an LLM chatbot, integrate OpenAI and Anthropic APIs using structured prompt engineering, streaming responses, and robust retry logic to handle technical questions effectively.

What is Retrieval-Augmented Generation and how does it work for AI applications?▼

Retrieval-Augmented Generation (RAG) enhances AI applications by chunking documents, generating embeddings, and storing them in pgvector to provide context-aware responses for LLM queries.

What's the best way to structure prompts for large language models?▼

The best way to structure prompts for large language models involves using structured templates, few-shot examples, and chain of thought processes to guide accurate model outputs.

How do I handle API errors and rate limits when integrating LLMs?▼

Handle LLM API errors by implementing retry logic and fallback patterns, ensuring robust error handling for OpenAI and Anthropic model integrations during streaming responses.

How do I manage token limits and caching for LLM-based automation?▼

Manage token limits and caching for LLM-based automation by following best practices for token management, implementing response caching, and conducting thorough prompt evaluation.

Can I use pgvector for embedding storage in a basic RAG pipeline?▼

Yes, you can use pgvector for embedding storage in a basic RAG pipeline to chunk documents, store vector representations, and retrieve context for large language model queries.

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