Learning Paths for Technical Professionals

RAG System Mastery

This starter learning path explores Retrieval-Augmented Generation (RAG) systems, covering naive and advanced RAG techniques, LlamaIndex integration with JavaScript, unstructured data preprocessing, knowledge graph-enhanced RAG, and multimodal RAG applications. Learners gain hands-on experience with vector databases, dense passage retrieval, cross-encoder re-ranking, knowledge graph construction using Neo4j, and building production-ready RAG applications with modern frameworks.

Skills:

RAG system design and optimization

Advanced retrieval techniques (query expansion, re-ranking, DPR)

RAG application development using LlamaIndex and JavaScript

Unstructured data preprocessing and chunking

Vector database management

Knowledge graph construction and querying (Neo4j)

Multimodal RAG application development

Full-stack chatbot and AI application deployment

Learning objectives

  1. Explain the principles and architecture of Retrieval-Augmented Generation (RAG), distinguishing between naive and advanced RAG techniques.
  2. Implement advanced RAG methods such as query expansion, cross-encoder re-ranking, and dense passage retrieval to improve information retrieval and answer generation.
  3. Build and deploy RAG applications using LlamaIndex and JavaScript, including full-stack chatbot development and integration with vector databases.
  4. Preprocess and structure unstructured data (PDFs, HTML, PPTX, images) for RAG and LLM applications using specialized frameworks and chunking strategies.
  5. Integrate knowledge graphs and multimodal data into RAG systems, leveraging tools like Neo4j and GPT-4 to enable complex, context-aware retrieval and recommendation workflows.

Target audience

This path is designed for AI engineers, data scientists, software developers, and technical professionals seeking to master RAG systems for real-world applications. It is suitable for those with foundational programming experience who want to build, optimize, and deploy advanced retrieval-augmented AI solutions using state-of-the-art tools and frameworks.

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