Editing Retrieval-Augmented Generation (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''RAG''' — Retrieval-Augmented Generation; an architecture combining retrieval of relevant documents with generation by an LLM.
* '''Retriever''' — The component responsible for finding relevant documents from a knowledge base given a user query.
* '''Generator''' — The LLM that reads retrieved documents and the user query to produce a grounded response.
* '''Knowledge base''' — The document collection from which the retriever fetches context; can be a vector store, search index, or database.
* '''Embedding''' — A dense vector representation of text that captures semantic meaning, enabling similarity-based retrieval.
* '''Vector store''' — A database optimized for storing and searching high-dimensional embedding vectors (e.g., Pinecone, Weaviate, Chroma, pgvector).
* '''Semantic search''' — Finding documents based on meaning similarity rather than keyword matching, using embedding vectors.
* '''Chunk''' — A segment of a larger document created during preprocessing. Documents are split into chunks before embedding.
* '''Context window''' — The maximum amount of text an LLM can process at once; RAG must fit retrieved chunks within this limit.
* '''Grounding''' — Providing the LLM with factual context (retrieved documents) to base its generation on, reducing hallucination.
* '''Hallucination''' — LLM-generated content that is factually incorrect or unsupported by evidence.
* '''Reranker''' — A model that reorders retrieved documents by relevance after initial retrieval, improving the quality of context passed to the LLM.
* '''HyDE (Hypothetical Document Embeddings)''' — A technique where the LLM generates a hypothetical answer, which is then embedded and used as the retrieval query.
* '''Naive RAG''' — The basic retrieve-then-generate pipeline without optimizations.
* '''Advanced RAG''' — RAG with pre-retrieval (query transformation) and post-retrieval (reranking, filtering) enhancements.
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