RAG

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:

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Next steps