RAG-based Chatbot System

A RAG-based Chatbot System is a document-focused chatbot system that uses rag technology (to provide document-grounded conversations).

• Context:
  • Task Input: User Query, Document Context
    • Optional Input: Conversation History, User Preferences
  • Task Output: Document-Grounded Responses
  • Task Performance Measure: RAG Quality Metrics including response accuracy, retrieval relevance, and conversation coherence
  • ...
  • It can be built using a RAG-based Chatbot Platform.
  • It can (typically) process Document Inputs through rag pipelines.
  • It can (typically) generate Vector Embeddings using rag models.
  • It can (typically) perform Vector Searches through rag databases.
  • It can (typically) create Context Responses via language models.
  • ...
  • It can (often) integrate with External Tools through system apis.
  • It can (often) track Usage Patterns via monitoring tools.
  • It can (often) customize Response Styles through parameter tunings.
  • ...
  • It can range from being a Simple RAG System to being an Advanced RAG System, depending on its implementation complexity.
  • It can range from being a Domain-Specific RAG System to being a General-Purpose RAG System, depending on its application scope.
  • ...
• Examples:
  • Specialized Systems, such as:
    • Research Analysis Systems for academic documents.
    • Educational Tutoring Systems for learning materials.
    • Medical QA Systems for clinical documents.
  • ...
• Counter-Examples:
  • Basic Chatbot Systems without document grounding.
  • Document Search Systems without conversation ability.
  • Vector Search Systems without natural language interfaces.
• See: RAG-based Chatbot Platform, Document Analysis System, Conversational AI System.

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References

2025

• https://www.galileo.ai/blog/mastering-rag-how-to-architect-an-enterprise-rag-system
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