RAG

RAG RAG (Retrieval-Augmented Generation) combines information retrieval with text generation — it pulls in an external knowledge base to make an LLM’s answers better. Foundational papers: Lewis, P., et al. (2020). “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.” NeurIPS. [paper] — the paper that started RAG Guu, K., et al. (2020). “REALM: Retrieval-Augmented Language Model Pre-Training.” ICML. [paper] Izacard, G., & Grave, É. (2021). “Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering.” EACL. [paper] — a different take on RAG 1. Context Window What it is The context window is the maximum amount of text an LLM can read and reason over in a single pass. ...