LLM

Enable backend chat completions with large language models via z-ai-web-dev-sdk.

Updated Jan 29, 2026

Search Tags:#backend #llm #chatbot #conversational-ai #z-ai-web-dev-sdk

Ready to run

npx skills add https://github.com/TIAN0517/iosboss --skill llm-tian0517

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: LLM
Source: https://github.com/TIAN0517/iosboss/tree/main/temp_extract/99999999/skills/LLM
Command: npx skills add https://github.com/TIAN0517/iosboss --skill llm-tian0517

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires z-ai-web-dev-sdk, and includes scripts (resource) components.

What problem does it solve?

This skill enables backend-driven chat completions with large language models, allowing developers to build robust conversational AI applications with controlled prompts and context.

Core Features & Use Cases

Multi-turn conversations with context management and system prompts to customize behavior.
Backend-first workflow using the z-ai-web-dev-sdk, suitable for chatbots, AI assistants, and content-generation tools.
Quick-start examples and practical patterns in the included scripts to accelerate development.

Quick Start

Use the provided scripts/chat.ts to run a basic chat example: initialize the SDK, construct a messages array with system and user messages, and call zai.chat.completions.create with thinking disabled to obtain a reply.

Frequently Asked Questions about LLM

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

FAQPage Schema

How do I implement backend chat completions with large language models?▼

Backend chat completions are implemented using the z-ai-web-dev-sdk to execute server-side inference. You initialize the SDK, construct a messages array with system and user roles, and call the chat completions endpoint to generate context-aware replies.

Can I manage multi-turn conversations and system prompts for AI chatbots?▼

Yes, multi-turn conversations are supported by passing message histories and system prompts to the backend. This allows you to maintain context across exchanges and customize the behavior of large language models for conversational AI applications.

Does this approach support context-aware responses for content generation apps?▼

Context-aware responses are fully supported by supplying message histories to the large language model. This mechanism enables AI assistants and content-generation tools to generate replies that reference prior user inputs and system instructions accurately.

What's the best way to start building conversational AI with the z-ai-web-dev-sdk?▼

The best way to start is by running the included example scripts. You initialize the SDK, define a messages array, and call the chat completions endpoint with thinking options disabled to quickly obtain your first AI-generated reply.

Do I need a specific SDK to run server-side chatbot completions?▼

Yes, you need the z-ai-web-dev-sdk to run server-side chat completions. This dependency handles backend execution for large language models, ensuring controlled prompts and context management for your AI applications.

Why use a backend-first workflow for AI assistants instead of client-side calls?▼

A backend-first workflow secures API keys and manages context directly on the server. Relying on the z-ai-web-dev-sdk for server-side execution prevents exposure of sensitive credentials and ensures robust conversational AI behavior.