Make AI Large Models Land: Complete Guide to RAG-Agent Development and Industry-Project Practice!

This course is not only suited for beginners but also helps experienced developers further improve AI-application ability. Through modularization, Agent design, Embedding technology, and other key knowledge, you will quickly master AI large models’ technical details and practical techniques in actual projects.

1. AI Large Models and RAG Technology Primer

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• Artificial-Intelligence Basics
  Understand artificial intelligence’s basic concepts, starting from the large-model evolutionary tree, master AI technology’s classification and development path.
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• RAG Technical Principles and Practice
  Deeply learn RAG technology’s core principles, and through actual cases take you hands-on practice, quickly getting started with RAG technology.
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2. In-Depth Embedding and Fine-Tuning Technology

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• Embedding Principles and Training
  Master Embedding technology’s core, understand Word2Vec, CBOW, and other classic models, and learn how to train and fine-tune Embedding models.
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• Fine-Tuning Dataset Preparation
  In this lesson, we teach you how to prepare fine-tuning datasets, and introduce SFT continued pre-training and preference-optimization techniques.
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3. Advanced RAG and Multi-Agent Architecture

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• Advanced RAG Technology
  Understand RAG’s advanced technologies, such as hierarchical indexing, sentence windows, sub-queries, etc., helping you improve retrieval efficiency and accuracy.
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• Langgraph Multi-Agent Architecture
  Learn how to achieve collaboration and intelligent processing in multi-Agent systems; this is crucial for solving complex problems.
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4. Project Practice and Case Analysis

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• Langchain and AutoGen Project Practice
  Through actual cases, learn Langchain project principles and AutoGen’s usage, improving your project-development ability.
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• Industry Application and RAG Landing Experience
  Understand how RAG technology is applied in actual industries; from technical landing to practice insights, helping you promote AI technology’s implementation in enterprises.
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5. Vector Databases and Similarity Search

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• Vector-Database Selection and Application
  In this part, you’ll learn how to choose a suitable vector database, and understand similarity search’s basic algorithms and actual applications.
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• RAG Evaluation and Optimization
  Learn how to evaluate RAG model performance, and master efficient optimization techniques, ensuring your model performs excellently in actual applications.
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Through this course, you’ll master comprehensive AI large-model technology from theory to practice, improving your career competitiveness. Whether you want to enter the AI industry, or hope to apply AI technology in your current work, this course provides you a solid technical foundation and practical experience.