Retrieval-Augmented Generation solves one of the biggest limitations of large language models: their knowledge is frozen at training time. RAG allows LLMs to pull in fresh, domain-specific information at query time by searching through a vector database of documents.
This means your AI can answer questions about your specific products, policies, or operations — not just general knowledge. At Informatica Systems, we use RAG in our National Foods chatbot to deliver accurate product information, recipe details, and promotional content.
RAG also dramatically reduces hallucinations — the AI cites its sources, making outputs more trustworthy and auditable. This is critical for enterprise deployments in regulated industries.