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14. RAG / Retrieval-Augmented Generation

Grounding generation in retrieved external documents. Children form an end-to-end pipeline: ingestion (parse → chunk → extract metadata → embed → store in vector DB), retrieval (semantic/keyword/hybrid search → rerank), and generation (context injection → grounded generation → citations/attribution). It exists to fix LLMs' two weaknesses: stale knowledge and hallucination. Leans directly on embeddings/similarity and on vector databases.

Children

  • source documents
  • parsing
  • chunking
  • metadata extraction
  • embeddings
  • vector database
  • semantic search
  • keyword search
  • hybrid search
  • retrieval
  • reranking
  • context injection
  • grounded generation
  • citations
  • source attribution