What is RAG (Retrieval-Augmented Generation)?
RAG, or Retrieval-Augmented Generation, is an AI pattern where a model retrieves relevant passages from a source document before generating an answer, grounding output in the source.
A language model can answer from patterns learned during training and may hallucinate. RAG adds a retrieval step: when the user asks a question, the system searches a corpus for relevant passages, supplies those passages as context, and then asks the model to answer.
The result can be more closely grounded in the retrieved context and can cite passages when the product exposes them. RAG is a common pattern for "chat with your document" features, support assistants over a help centre, and AI search tools that attribute claims.
RAG quality depends on three pieces: how the source is chunked (paragraph, section, sentence), how chunks are embedded for similarity search, and how the model is prompted to use the retrieved context. A weak link in any of these produces answers that are technically grounded but practically wrong.
Summio uses retrieval to bring relevant passages from available source context into summary and chat workflows. Citations are shown when available, but readers should still verify the original before relying on important details.
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How is RAG different from fine-tuning?
Fine-tuning bakes new knowledge into the model weights — slow, expensive, and only updateable by re-training. RAG keeps the model frozen and changes the retrieved context per query, so updating knowledge means re-indexing a document, not retraining anything.
Does RAG eliminate hallucinations?
No. Retrieved context can help, but the model can still misread a passage, ignore it, or combine correct snippets into an incorrect synthesis. Important details still need verification against the original source.
Where is RAG used?
AI search engines (Perplexity, ChatGPT search, Claude with web access), enterprise document chat, customer-support bots over a help centre, and reading apps like Summio. Anywhere an answer needs to be attributable to a specific source.