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RAG (Retrieval-Augmented Generation) is the technique that grounds InsightHub’s answers in your documents rather than relying on what the language model learned during training.

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How it works

Instead of asking the AI to answer from memory, RAG works like this:
  1. Your question is converted into a vector — a numerical representation of its meaning
  2. The system searches the topic’s indexed documents for passages semantically similar to your question
  3. The most relevant passages are included in the AI’s context
  4. The AI generates its answer using those passages as reference material
The result is an answer drawn from your actual documents, with traceable sources.

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What you see in InsightHub

During an exploration, the Thinking indicator shows you:
  • Retrieved passages — which document chunks were fetched for your query
  • Source attribution — which document each passage came from
  • Tool steps — any additional calls the AI made to gather information
This transparency is intentional: you can see where an answer came from and notice when the AI is filling gaps versus citing your documents directly.

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Why document quality matters

Retrieval is only as good as the documents you provide:
FactorImpact
RelevanceDocuments that match the topic’s subject produce better results than loosely related files
Text qualityClean text extracts more accurately than scanned images or heavily formatted PDFs
CoverageIf the answer is in a document you haven’t uploaded, the AI cannot retrieve it
If answers are vague or off-target, review the documents in your topic. Add missing files, verify all documents have Ready status, and make sure your question falls within the scope of the available content.

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Next steps

Documents

Manage the documents that RAG searches during explorations.

Exploration

See RAG in action during a live conversation.