【AI System Bootcamp】Build Intelligent Agent Applications

From Large Model Principles to Deployment, Build Your AI System

Master the complete skill chain from large model API calls, Prompt engineering design, to LangChain+RAG system development and deployment, creating deployable AI systems for demonstration.

8 weeks
Small class of 10 Students
4.8 Rating
Enroll Now - ¥7,999Free Demo

Course Information

Difficulty Level:Advanced Practice
Language:Chinese
Format:Small Class Live + Project Coaching
Duration:8 weeks

Course Objectives

Master LLM architecture and API calling processes
Master the five principles of Prompt engineering design
Proficiently use LangChain/LangGraph frameworks
Build RAG systems and vector databases
Design multi-tool Agent system architecture
Complete AI project deployment and launch

Prerequisites

Familiar with basic Python syntax
Have API calling or Web development basics
Basic understanding of AI/LLM

Course Schedule

1

LLM Principles and API Calling Practice

  • LLM Architecture Overview (Transformer, Decoder-only structure)
  • GPT/Claude/LLaMA API calling process
  • Token mechanism and Token cost estimation
  • OpenAI / Anthropic API calling practice (Python + Postman)
Practice Project: Call different model APIs, output comparative analysis of consistent task results
2

Prompt Engineering Fundamentals

  • Five principles of Prompt design (clarity, format, instruction tone, context setting, output format)
  • Few-shot Prompt vs Zero-shot comparison
  • Chain of Thought (CoT) reasoning strategy
  • Roleplay/Persona Prompt model personality setting
Practice Project: Design multiple Prompts to complete the same task effectiveness comparison experiment
3

LangChain Framework Practice

  • LangChain core components: LLMWrapper, PromptTemplate, Chains
  • Memory modules: ConversationBufferMemory / SummaryMemory
  • Tool integration: Calculator, Search, CustomTool encapsulation
  • Agent vs Chain usage scenario analysis
Practice Project: Build multi-turn dialogue Bot with basic memory function and tool calling capability
4

RAG System Construction and Optimization

  • Vector database principles (FAISS / Chroma / Weaviate)
  • Embedding technology (OpenAI, HuggingFace)
  • Document splitting and preprocessing (LangChain Document Loader)
  • Retriever + RAG Chain construction
  • RAG optimization techniques: Chunk size, Query Rewriting, retrieval filtering
Practice Project: Knowledge base Q&A system: upload documents or knowledge materials, users can ask freely
5

LangGraph Advanced Workflow Orchestration

  • LangGraph framework introduction: graph state modeling
  • Build multi-step workflows: conditions, loops, interrupt handling
  • Multi-model collaboration task allocation and tracking
  • Agent state transition management
Practice Project: Design multi-step task (such as article writing, image generation) flowchart, implement automatic execution
6

Agent System Architecture Design

  • Monolithic Agent vs multi-Agent architecture
  • Tool calling logic (Tool Calling, Tool Description)
  • Communication and scheduling between Agents
  • Multi-modal Agent integration (text + image)
Practice Project: Intelligent assistant Agent: user input questions, Agent intelligently allocates tasks and responds based on context and tool calls
7

System Optimization and Deployment Launch

  • Cost evaluation and caching mechanism (LangSmith + LangChain Callbacks)
  • Performance tuning and debugging
  • Streamlit rapid interactive interface construction
  • Vercel/Github Pages deployment strategy
  • User behavior logging and usage analysis
Practice Project: Build frontend UI for the aforementioned projects and deploy online, generate public links for demonstration
8

Project Integration · Demo Presentation

  • Project integration and multi-function combination
  • Frontend + backend collaborative design
  • Write project documentation and technical blog
  • Record project explanation video
  • Mock defense and feedback
Practice Project: Complete AI application launch

Individual Projects

My AI Assistant v1.0

Build an AI assistant that supports multi-turn dialogue, tool calling, knowledge Q&A, and task management

Skill Requirements:

LangChain Chain & Agent systemCustom Prompt designVector database + document retrievalStreamlit UIAPI encapsulation and online deployment

Deliverables:

  • Interactive web Demo (Vercel/GitHub Pages)
  • Project GitHub repository (code + README)
  • Video explanation (5 minutes)
  • Technical documentation (PDF)

Team Project

AI Customer Service Intelligent Assistant (3-person group)

Build a RAG customer service system that can automatically answer user questions, integrating FAQ, tool calling, and user profile modules.

Skill Requirements:

LangGraph + AgentEmbedding + RetrieverProject collaboration management (Notion + Git)

Deliverables:

  • Project running Demo + online link
  • Project division of labor record document
  • Group explanation video
  • Complete PPT presentation materials

Achievements

Complete personal project: build 1 complete AI Agent system and launch online
Complete collaborative project: participate in 1 RAG customer service Agent project and publish demonstration
GitHub portfolio: submit project repository, including documentation, code, deployment instructions, etc.
Obtain advanced certification: master LangChain / Prompt / RAG / Agent and other technologies

Certificate

【AI System Bootcamp】Build Intelligent Agent Applications Certification

Professional certification obtained after completing 8 weeks of course study and successfully deploying AI projects

Certificate Skills:

LLM calling and architecture understandingPrompt engineeringLangChain/LangGraph applicationAgent development and deployment

Course Consultation

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