RAG Chatbot

What is RAG?

Retrieval-Augmented Generation (RAG) is an AI architecture that grounds large language models in your actual data. Instead of relying solely on pre-trained knowledge, a RAG system retrieves relevant passages from a document corpus at query time and feeds them to the model as context. The result: answers that are accurate, up-to-date, and backed by verifiable sources.

How it works

1. Upload -- Drop a PDF, TXT, or Markdown file. The system extracts text, splits it into overlapping chunks, and generates vector embeddings for each chunk.
2. Ask -- Type a question in the chat. Your query is embedded with the same model and compared against all stored chunks using cosine similarity.
3. Retrieve -- The top matching chunks are injected into the model's prompt as numbered sources.
4. Generate -- The language model produces an answer grounded in those sources, with inline citation markers like [1], [2].
5. Verify -- Click any citation to open the original document at the exact passage, so you can confirm the answer yourself.

Why it matters for enterprise

• Accuracy over hallucination -- RAG constrains the model to documented facts, dramatically reducing fabricated answers.
• Source transparency -- Every claim links back to the original document and page, satisfying audit and compliance requirements.
• Private data, no fine-tuning -- Query proprietary documents without exposing them to model training or expensive fine-tuning cycles.
• Always current -- Unlike a frozen model, RAG reflects whatever documents are in the corpus right now.
• Cost-efficient -- Embedding and retrieval are far cheaper than fine-tuning or maintaining bespoke models.

What you can do here

• Without an account -- Chat against pre-loaded content covering AI strategy, training modules, and blog posts. See how citations and source linking work in practice.
• With an account -- Upload your own PDFs, text files, or Markdown documents. Ask questions across your personal document library and the shared defaults. Delete documents when you're done.

Try it out

Open the RAG Chatbot