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Useful RAG: much more than PDF chat

A serious RAG setup isn't about uploading a PDF and hoping for miracles. It's a data pipeline: it cleans documents, retrieves relevant context, cites sources, and refuses to answer when it doesn't know.

  • Understand the real components of a RAG system.
  • Distinguish a slick demo from a system usable at an SMB.
  • Define quality criteria before indexing documents.

The complete pipeline

TerminalCode
Documentos
  -> ingesta y limpieza
  -> chunking
  -> embeddings
  -> base vectorial
  -> recuperación
  -> reranking
  -> generación con citas
  -> evaluación y logs

Design questions

  • Which documents can each user query?
  • Does every answer need an exact citation?
  • What happens if it doesn't find evidence?
  • How are outdated documents updated?
  • Which inputs might contain malicious instructions?