An interactive simulator showing how Retrieval-Augmented Generation grounds LLM reasoning in external knowledge, reducing hallucinations and connecting to proprietary data.
The RAG pattern provides the specific mechanism for grounding an agent's reasoning in external, factual knowledge. It is one of the most effective and widely adopted patterns for enhancing performance, reducing hallucinations, and connecting to proprietary or real-time information.
"Invest in a robust document processing pipeline that cleans and chunks your source data effectively. For complex queries, consider implementing agentic RAG, where a dedicated agent can refine the user's initial query, perform iterative searches, and synthesize information from multiple retrieved passages before generating a final answer."
This simulator shows two pipelines: Ingestion (documents are chunked, embedded, and stored in a vector database) and Query (your question is embedded, similar chunks are retrieved, and the LLM generates a grounded answer).
Toggle between Naive and RAG-enhanced modes to see how retrieval dramatically improves response quality. Type a question below and watch the data flow through each pipeline stage in real time.