ki-konzepte RAG
Agentic RAG
Agentic RAG is a RAG architecture in which an agent drives retrieval — it decomposes the question, searches iteratively, checks the hits, and decides itself whether a second or third pass is needed.
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RAG
Retrieval-Augmented Generation — Konzepte, Embeddings, Chunking, Reranking.
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- ki-konzepte RAG
Chunking
Chunking is the splitting of longer texts into smaller, self-contained pieces — the first step of any RAG pipeline because embeddings and retrieval work at the chunk level.
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Contextual Retrieval
Contextual Retrieval is an Anthropic RAG technique that prepends an LLM-generated context sentence to every chunk before indexing — sharply raising retrieval quality.
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Cosine Similarity
Cosine similarity measures how similar two vectors are by the angle between them — the default metric in RAG retrieval for comparing query embeddings against the embeddings stored in a vector index.
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GraphRAG
GraphRAG is a RAG variant that uses a knowledge graph in addition to vector search — answers come from linked entities and relationship paths, not just similar text.
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Hybrid Search
Hybrid search combines lexical full-text search (e.g. BM25) with semantic vector search and unites the strengths of both — precise on keywords and robust on meaning.
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HyDE
HyDE (Hypothetical Document Embeddings) is a RAG technique where an LLM first drafts a fake answer to a query — and then uses that answer's embedding to find real documents.
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Query Expansion
Query expansion enriches the original query in a RAG pipeline with synonyms, reformulations or hypothetical answer texts in order to lift retrieval recall.
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Reranking
Reranking reorders an already-retrieved list of hits with a more accurate model — typically a cross-encoder that scores query and each candidate together, instead of just comparing vector distances.
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Semantic Search
Semantic search retrieves content by meaning rather than exact word match — query and documents are compared as embeddings, so synonyms and paraphrases also hit.
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Vector Database
A vector database stores embeddings as high-dimensional vectors and searches them by semantic similarity — the core infrastructure for RAG, semantic search, and recommendation systems.
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