As incredible as large language models are, enterprises can't take full advantage. Even the most common use cases—customer service chatbots, marketing writing and code assistance with Copilot—aren't reliable. In February 2024, Air Canada wasHow can LLMs break outside of their three constrained use cases and become more trustworthy and accurate? The answer lies in giving LLMs safe, protected access to data outside of what they're trained upon.
Data-centric AI, also known as runtime AI, enables an LLM to fetch valid, real-time data outside of the model to inform its answers. The LLM uses an API to pose queries to external sources—for example, to an SQL database. After receiving an answer, the LLM incorporates it into its reply. By inserting an organization intermediary to execute a query on behalf of the user, you ensure the LLM is neither trained with your organizational data nor leaking data into third-party databases.
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