> ## Documentation Index
> Fetch the complete documentation index at: https://docs.alquimia.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# RAG
> How retrieval-augmented generation works in InsightHub explorations.
**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.
## 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.
## 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.
## Why document quality matters
Retrieval is only as good as the documents you provide:
| Factor | Impact |
| ---------------- | ------------------------------------------------------------------------------------------ |
| **Relevance** | Documents that match the topic's subject produce better results than loosely related files |
| **Text quality** | Clean text extracts more accurately than scanned images or heavily formatted PDFs |
| **Coverage** | If 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.
## Next steps
Manage the documents that RAG searches during explorations.
See RAG in action during a live conversation.