Learn key methods for representing text in RAG systems. Explore why text representation matters, implement a Bag-of-Words model, understand how embeddings capture deeper meaning, visualize embeddings with t-SNE, and compare BOW and embeddings in document retrieval and semantic search.

Scale up your RAG system by building and querying a vector database. Learn to preprocess documents, store chunk embeddings in ChromaDB, retrieve relevant chunks, and construct prompts that can handle multiple context chunks. Additionally, see how to manage updates to your collection and how to approach large-scale ingestion using batch strategies.

Advance your RAG pipeline by integrating hybrid retrieval methods that combine BM25 and embeddings, implementing iterative retrieval with query refinement, and summarizing multiple context chunks when needed. Learn to constrain LLM outputs to rely strictly on retrieved context, and apply advanced error handling, fallback strategies, and logging to ensure accuracy and reliability in your system.