RAG (Retrieval-Augmented Generation) is the technique that enriches an LLM's responses with information dynamically retrieved from a specific knowledge base — internal documents, manuals, FAQs, interaction history. Instead of the LLM answering only with what it learned in training (potentially outdated or generic), it first consults the customer's base and composes the answer based on that information.
RAG solves two problems at once: it lets LLMs answer about content that was not in the training (internal documents, proprietary data, recent information) and it reduces hallucination (if the base does not have the answer, the model is instructed to say 'I don't know' rather than invent). It is the dominant architecture for corporate knowledge assistants — an internal assistant that answers employees' questions about policies, products, processes.
INFOS implements RAG for customers with significant knowledge bases — technical manuals, internal policies, product documentation. Integration with AutomationForge lets generated articles consult the customer's body of knowledge before being produced. Governance — who accesses what, response auditing, anonymisation — is handled explicitly in each implementation.