Artificial Intelligence
Retrieval-augmented generation: chunking, embeddings, ranking.
Why an LLM sometimes needs external knowledge, what retrieval adds, and how RAG changes the generation pipeline.
How text becomes vectors, how semantic similarity supports retrieval, and why vector databases are useful in RAG systems.
Connect documents, chunking, embeddings, retrieval, prompting and generation into one end-to-end RAG pipeline.