Language models excel in generating engaging text, and are ideal as the base for agents. Agents provide users with an intuitive chat-based application to receive assistance in their work. When designing an agent for a specific use case, you want to ensure your language model is grounded and uses factual information that is relevant to what the user needs.
Though language models are trained on a vast amount of data, they may not have access to the knowledge you want to make available to your users. To ensure that an agent is grounded on specific data to provide accurate and domain-specific responses, you can use Retrieval Augmented Generation (RAG).
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