RAG works by pairing a large language model with a retrieval step: when a customer asks a question, the system first searches an external knowledge base - documents, product catalogues, policy pages - for the most relevant passages, then feeds those passages to the model alongside the original question so its answer is grounded in current, verifiable source material. This avoids the need to retrain the model every time underlying business information changes.
For an AI concierge operating across banking, retail, healthcare, and logistics in the Gulf, product details, return policies, and service terms change frequently and often exist in both Arabic and English versions. RAG lets the AI stay accurate as that content updates, without expensive retraining cycles, and reduces the risk of the model inventing an answer when it does not actually know a business's current policy, which matters most in regulated, compliance-sensitive industries.
In Eshal: Eshal uses RAG for product catalogues, return policies, and FAQ content, combined with live API calls for real-time operational data such as order status or account balances. This combination lets the AI concierge give grounded, up-to-date answers drawn directly from a customer's own documentation rather than relying purely on the model's general training data, reducing the risk of inaccurate responses.