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.
Learning objectives
- Explain the principles and architecture of Retrieval-Augmented Generation (RAG), distinguishing between naive and advanced RAG techniques.
- Implement advanced RAG methods such as query expansion, cross-encoder re-ranking, and dense passage retrieval to improve information retrieval and answer generation.
- Build and deploy RAG applications using LlamaIndex and JavaScript, including full-stack chatbot development and integration with vector databases.
- Preprocess and structure unstructured data (PDFs, HTML, PPTX, images) for RAG and LLM applications using specialized frameworks and chunking strategies.
- 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.