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llm-integration

Document patterns for integrating LLMs with API usage, streaming, and RAG pipelines.

100|18|Updated Apr 3, 2026
Search Tags:#streaming#cost optimization#LLM integration#function calling#API usage#RAG pipeline
One-click install
npx skills add https://github.com/Mybono/ai-orchestrator --skill llm-integration-mybono
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-integration
Source: https://github.com/Mybono/ai-orchestrator/tree/main/skills/llm-integration
Command: npx skills add https://github.com/Mybono/ai-orchestrator --skill llm-integration-mybono

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a comprehensive guide for integrating Large Language Models (LLMs) into applications, offering patterns for API usage, streaming, function calling, RAG pipelines, and cost optimization, thereby simplifying the process of incorporating LLM capabilities.

Core Features & Use Cases

  • API Client Pattern: Offers a TypeScript example for creating and managing messages with an LLM.
  • Streaming Responses: Demonstrates how to stream responses from an LLM for efficient processing.
  • Function Calling (Tool Use): Illustrates how to integrate tool use with LLMs for enhanced functionality.
  • RAG Pipeline: Explains the process of building and utilizing Retrieval Augmented Generation pipelines.
  • Document Chunking: Describes how to split documents into manageable chunks for LLM processing.
  • Cost Optimization: Provides strategies for selecting the right model and optimizing costs.
  • Use Case: For a software development team looking to incorporate LLMs for code generation or documentation, this Skill offers a structured approach to streamline the integration process.

Quick Start

Run the llm-integration skill to get started with integrating LLMs into your application.

Frequently Asked Questions about llm-integration

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

FAQPage Schema
How do I integrate LLM API usage into my application?▼

LLM integration uses an API client pattern to manage messages and send requests. This approach provides a structured TypeScript example for creating and managing messages with an LLM to simplify incorporating capabilities.

What's the best way to stream LLM responses for efficient processing?▼

Streaming LLM responses enables efficient processing by sending data in chunks as it is generated. This pattern demonstrates how to stream responses from an LLM directly to your application for real-time output.

How does function calling work with LLMs?▼

Function calling with LLMs integrates external tool use to enhance functionality. It illustrates how to connect specific tools to the model, allowing the LLM to trigger functions and retrieve structured outputs.

How do I build a RAG pipeline and handle document chunking?▼

Building a RAG pipeline involves splitting documents into manageable chunks for LLM processing. This process explains how to build and utilize Retrieval Augmented Generation pipelines to ground model responses.

What are the best strategies for LLM cost optimization?▼

LLM cost optimization strategies focus on selecting the right model for your specific task. It provides actionable approaches to optimize costs while maintaining the required functionality for your application's API usage.

Do I need prior knowledge of LLMs for application integration?▼

Yes, this LLM integration process requires existing knowledge of LLMs and application integration. It is designed for software development teams looking to incorporate LLMs for tasks like code generation or documentation.

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