AI Full-Stack Training Camp

Empowering developers to leap forward and embrace the next wave of the AI era.

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Professional Courses

Course content meticulously designed by senior developers.

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Learning Path

Visualized learning path map for a clear skill development trajectory.

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Project-Oriented

Combining theory with practice to build real project experience.

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Detailed Videos

High-quality video teaching, explaining core concepts in an accessible way.

Learning Path Map

Clear learning path guidance to help you systematically master AI development skills, from basics to practical application.

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Vibe Coding

Foundation

Application

Optimization

Training Modules

A selection of nine core technology areas to build a complete development skill set.

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2 Day Course

Vibe Coding

As AI increasingly permeates the development process, traditional programming methods are quietly changing. Vibe Coding is a new programming paradigm that adapts to the AI era. It not only emphasizes code output but also focuses on efficient collaboration and human-computer co-creation with intelligent agents (such as LLM, Copilot, Claude, etc.). Through two days of high-intensity training, team members will fully understand the core concepts of Vibe Coding, master key tools and practical techniques, and be able to effectively apply them to the actual development process to improve development efficiency and code quality.

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3 Day Course

LLM Development Basics

This course is for developers with development experience but no prior knowledge of the LLM field. The course aims to help developers systematically master the core principles, component composition, security risks of large language models (LLM), and the practical application of mainstream development frameworks (LangChain & LangGraph). From theoretical introduction to project demonstration, the course proceeds step by step, taking into account both technical breadth and engineering implementation.

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2 Day Course

Prompt Engineering

This course is for developers who have completed the LLM Basic training and want to learn the systematic method of Prompt design and optimization. From basic concepts to advanced patterns, from manual design to automated tools, this course helps students master the complete skill system of Prompt Engineering and improve the effectiveness and reliability of LLM applications.

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2 Day Course

RAG Full-Stack Development

This course is for developers with some AI and programming background, focusing on the principles, system composition, security considerations, and implementation of Retrieval-Augmented Generation (RAG). You will systematically learn the working mechanism of LLM and RAG, and master the complete E2E project construction method, including key modules such as index construction, query optimization, and permission control. The course adopts a combination of theory and practice to help you develop the ability to independently develop and deploy enterprise-level intelligent question-answering systems.

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3 Day Course

Agent in Depth

This course is for developers, researchers, and product managers interested in large language models (LLMs) and their intelligent agents. The course covers the basic principles of LLMs, the composition of Agents, common protocols and tools, and end-to-end Agent project construction methods. Through a combination of theoretical explanations and practical exercises, the course helps students master the core skills of building intelligent Agent systems from scratch.

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2 Day Course

Generative UI Development

This course is for developers who have completed the LLM Basic, RAG, and Agent in Depth training, and want to learn the design concepts and implementation methods of generative user interfaces. From theoretical foundations to practical applications, this course helps students master the core skills of how Agents and UIs work together to build intelligent user interaction experiences.

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2 Day Course

Deep Research System

This course is for developers and researchers who have completed the LLM Basic, RAG, and Agent in Depth training. It provides in-depth study of the design and implementation of deep research systems based on multi-agent collaboration. Through systematic theoretical learning and practical exercises, you will master the core skills of building complex research Agent systems using LangGraph, including key links such as problem decomposition, information retrieval, content integration, and reflection and optimization.

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LLM Evaluation and Performance Optimization

This course focuses on the evaluation methodologies and performance optimization techniques for large language models. You will learn how to build a scientific evaluation framework, design multi-dimensional evaluation metrics, conduct systematic performance testing, and optimize models and prompts based on evaluation results. The course covers the complete methodology from single-point evaluation to end-to-end system evaluation, helping you build comprehensive control over AI system quality.

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LLM Finetuning Techniques and Engineering Practices

This course delves into the finetuning techniques and engineering practices for large language models. From LoRA and QLoRA to full finetuning, you will systematically learn the principles and application scenarios of various finetuning methods. The course will also cover key aspects such as data preparation, training process monitoring, and effect evaluation, and help you master the complete process and best practices of enterprise-level model finetuning through hands-on projects.

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Training Modules

15+

Training Days

150+

Learning Hours

100%

Real Projects