AI工程化实战2026：从ChatGPT到RAG管道与智能体系统

本课程面向已具备Python基础并希望从使用AI工具转向构建生产级AI系统的开发者，系统讲解AI工程化的核心架构与实践方法。课程采用动手实战方式，涵盖本地大语言模型部署与验证、智能体可控执行循环构建、可靠的检索增强生成管道实现、结构化输出强制约束、以及将接口、引擎、路由与记忆层分离的清晰架构设计。重点聚焦混合AI系统设计，结合本地模型与云端API，并引导学员基于系统架构而非炒作来评估各类AI框架。通过课程学习，学员将掌握构建可观测、可扩展、生产就绪的AI系统所需的核心工程原则，具备独立设计与长期维护能力。适合已使用过ChatGPT或LLM API、希望进一步深入系统层面的开发者。

制作人：Rivan Valen
MP4格式 | 视频：h264，1280×720 |
级别：所有级别 | 语言：英语 | 时长：5节课（1小时18分钟） | 文件大小：721 MB

AI Engineering 2026: ChatGPT, RAG & Agentic Systems

Build AI Agents, Production RAG Pipelines & Hybrid LLM Systems with Python and Local + Cloud Models

AI Engineering in 2026 is no longer just about prompts — it’s about building AI Agents, RAG pipelines, and production-ready LLM systems.

This course is designed to be hands-on. Instead of just explaining AI concepts, we’re going to install tools, run models locally, and experiment with the systems that power modern AI engineering.

In this course, you’ll move from using tools like ChatGPT to engineering real AI architectures with agents, RAG, structured outputs, and hybrid routing that combine local models, cloud APIs, RAG pipelines, and agentic workflows.

You’ll start by running your own local LLM and validating exactly how it communicates. From there, you’ll build a simple AI assistant and then progressively evolve it into a structured, observable system.

You’ll learn how to

• Build AI Agents with controlled execution loops

• Implement reliable RAG (Retrieval-Augmented Generation) pipelines

• Enforce deterministic outputs using structured schemas

• Separate interface, engine, routing, and memory into clear architectural layers

• Design hybrid AI systems that combine local and cloud models

• Evaluate AI frameworks based on system design rather than hype

This course is designed as a practical AI engineering course for developers who want to understand what happens between “prompt” and “production” in real-world systems.

If you’ve experimented with ChatGPT or LLM APIs and want to move toward building scalable, production-ready AI systems with confidence and clarity, this course is for you.

By the end, you won’t just be using AI tools — you’ll be designing reliable, observable, production-ready AI systems you actually understand.

What you’ll learn
✓ Build AI Agents and LLM-powered applications
✓ Design hybrid AI systems combining local and cloud models
✓ Evaluate AI frameworks architecturally rather than relying on hype
✓ Apply AI Engineering principles for scalability and long-term ownership

Requirements
● To get the most out of this course, you should have: Basic Python knowledge (functions, loops, simple scripts) Comfort using a terminal (PowerShell, Bash, or macOS Terminal) A basic understanding of APIs and HTTP requests A modern laptop (16GB RAM recommended for running local models)
● Recommended (but not required): Ability to run a Linux environment (WSL on Windows or a lightweight VM) Familiarity with JSON and structured data Curiosity about how AI systems work beyond prompt engineering
● You do not need: Prior machine learning or deep learning experience Advanced math or statistics Enterprise DevOps background Previous experience with AI agents