If a company wants AI-generated responses based on case history, which grounding method should they use?

When a company aims to generate AI responses based on case history, the ideal approach is CRM Data Grounding. This method utilizes specific data stored in the Customer Relationship Management (CRM) system, which includes detailed information about past interactions, customer queries, and case histories. By grounding AI responses in this comprehensive database of historical data, the AI can tailor its replies to reflect real customer experiences, ensuring the responses are relevant, personalized, and contextually accurate.

Using CRM Data Grounding allows the AI to pull in precise details about previous cases, such as resolutions, customer sentiments, and interaction patterns. This contextual knowledge is critical for producing informed responses that align with both the company’s knowledge base and the customer’s history. In this manner, customer service representatives can enhance their productivity and service quality, benefiting both the company and its clients.

In contrast, other methods like Retriever Grounding might rely more generally on fetching documents or data rather than tailoring responses from a relational database like a CRM. Example Grounding focuses on generating responses based on provided examples rather than specific, contextual data, which reduces specificity. Instructional Grounding employs directive formats to guide the AI on how to respond, without directly incorporating the rich insights derived from actual case histories.