In traditional RAG (Retrieval-Augmented Generation), you use an LLM at runtime to read those retrieved documents and synthesize a direct, conversational answer

In traditional RAG (Retrieval-Augmented Generation), you use an LLM at runtime to read those retrieved documents and synthesize a direct, conversational answer for the user. That generative step is what incurs an API cost and adds latency per query. RAGless gives you that direct Q&A experience (the synthesized answer) but removes the runtime generation by pre-computing all the answers offline.