Retrieval-augmented generation (RAG) is an AI architecture that combines retrieval — fetching relevant information from a knowledge base — with generation — producing a response using a language model. The retrieved information is injected into the LLM's context before generation, grounding the response in real data.
Try Lucy OS1 →Without RAG, an LLM can only use knowledge baked in during training — which has a cutoff date, may contain errors, and cannot include private information. RAG systems maintain a separate vector database of documents, memories, or facts. When a user asks a question, the system retrieves the most semantically relevant documents and provides them to the LLM as context. This dramatically reduces hallucinations and enables real-time information access.
Lucy OS1 uses RAG for memory retrieval. When you start a conversation, Lucy searches your memory database for relevant context — past conversations, preferences, goals — and injects them into the system prompt before responding. This is how Lucy 'remembers' you across thousands of conversations without a context window large enough to hold all of them.
Try Lucy OS1 →Documents and memories are converted to numerical vectors (embeddings) that capture semantic meaning. Similar meaning = close vectors. This enables semantic search across the knowledge base.
A specialised database optimised for storing and searching high-dimensional vectors. Popular options: Pinecone, Weaviate, pgvector (PostgreSQL extension).
Finding relevant content by meaning rather than keyword match. 'My weekly standup' retrieves relevant memories even if the exact phrase was never used before.
Retrieved documents are formatted and injected into the LLM's context window as additional background information before the user's query is processed.
Is RAG the same as AI memory?
RAG is the mechanism that powers retrieval-based AI memory. The memory system stores information; RAG retrieves the relevant subset for each conversation.
Does RAG eliminate hallucinations?
RAG significantly reduces hallucinations for knowledge-retrieval tasks by grounding generation in retrieved facts. It does not eliminate hallucinations entirely — the LLM can still confabulate or misinterpret retrieved content.
What is the difference between RAG and fine-tuning?
Fine-tuning bakes knowledge into model weights (static, expensive to update). RAG retrieves knowledge dynamically from an external source (easy to update, no retraining needed). RAG is generally preferred for frequently changing information.
Can RAG use real-time information?
Yes — if the knowledge base is kept current. Some RAG systems index real-time web search results. Others use databases that update on a schedule. The retrieval step can access any data source that is accessible at query time.
Lucy OS1 puts these concepts to work in a real, streaming voice AI pipeline — Deepgram STT, GPT-4o-mini, and Cartesia TTS delivering natural voice conversation.
Start talking to Lucy →Welcome