Welcome to Module 7 — RAG & AI Knowledge Systems.

In this lesson, you will learn how to connect AI models with external and private knowledge so they can provide more relevant, contextual, and up-to-date responses. You will understand how Retrieval-Augmented Generation (RAG) works and how documents, embeddings, vector databases, retrieval, and Large Language Models work together to build knowledge-based AI applications.

Topics Covered:

1. What is RAG?
   Understand Retrieval-Augmented Generation and why retrieval is useful when an AI model needs information that may not be available in its internal knowledge.
2. Why RAG is Needed
   Learn how RAG can help AI applications work with private documents, company information, specialized knowledge, and frequently changing information.

3. RAG Architecture
   Understand the complete RAG pipeline:

   Documents → Processing → Chunking → Embeddings → Vector Database → Retrieval → Context → LLM → Response

4. Document Loading
   Learn how different sources such as PDFs, text files, websites, and other documents can be prepared for use in a knowledge system.
5. PDF Processing
   Understand how PDF documents are extracted and prepared before they can be searched and used by an AI system.
6. Document Splitting and Chunking
   Learn why large documents are divided into smaller chunks and how chunk size and overlap can affect retrieval quality.
7. Embeddings
   Understand how text is converted into numerical vector representations that capture semantic meaning and relationships.

8. Vector Databases
   Learn how vector databases store and search embeddings efficiently.

   Examples include:

   • FAISS
   • Chroma
   • Qdrant
   • Pinecone
   • Weaviate
9. Semantic Search
   Understand how AI systems can search based on meaning rather than relying only on exact keyword matches.
10. Keyword and Hybrid Search
    Learn how keyword-based and semantic search can be combined to improve information retrieval.
11. Metadata Filtering
    Understand how metadata such as document type, source, category, date, or department can be used to refine search results.
12. Retrieval and Context Construction
    Learn how relevant information is retrieved and provided to an LLM as context before generating a response.
13. Reranking
    Understand how retrieved results can be reordered to identify the most relevant information before sending it to the language model.
14. Query Rewriting
    Learn how a user's question can be transformed into a better search query to improve retrieval accuracy.
15. RAG with LLMs
    Understand how retrieved information is combined with an LLM to generate grounded and context-aware responses.
16. Advanced RAG
    Explore advanced approaches such as:
    • Advanced Retrieval
    • Hybrid RAG
    • Query Transformation
    • Reranking
    • Agentic RAG
    • Graph-Based RAG
17. RAG Frameworks
    Get introduced to popular frameworks and tools used for building RAG applications, including:
    • LangChain
    • LlamaIndex
18. Reducing Hallucinations
    Understand how retrieval, context quality, source grounding, and response validation can help improve the reliability of AI-generated answers.
19. RAG Evaluation
    Learn the importance of evaluating retrieval quality, relevance, response accuracy, and overall system performance.
20. Real-World RAG Applications
    Explore practical applications such as:
    • Company Knowledge Assistants
    • PDF Question-Answering
    • Customer Support
    • Internal Documentation Search
    • Research Assistants
    • Legal and Policy Document Search
    • Enterprise Knowledge Systems

Major Project:
Build a PDF/Company Knowledge RAG Assistant that allows users to upload documents and ask questions about their content. The system should process documents, create embeddings, store them in a vector database, retrieve relevant information, and generate contextual answers using an LLM.

Learning Outcome:
By the end of this lesson, you will understand the complete RAG architecture and how documents, chunking, embeddings, vector databases, retrieval, and LLMs work together. You will be able to design the foundation of a knowledge-based AI system capable of answering questions using external or private information.

Lesson Resources:
Video Lesson + Presentation PDF + Quize