For example, I recently supported Salesforce’s Einstein Bots modularity effort with teams in Service and Commerce Cloud. The feature aims to create out-of-the-box functionality for Einstein Bots either as a fully fledged bot or as an addition to an existing bot with only some adjustments. Because each block or template could be used by customers of any industry for bots of any type, we had to decide between a specific persona or a blank slate. We chose the latter. While bot admins can tailor their bot dialog, we decided to keep our conversational patterns sufficiently broad. We included universal traits, like enthusiasm, that people prefer in service experiences.
But deciding a bot should be enthusiastic is different from designing an enthusiastic bot. How does enthusiasm appear in sentences, especially if a bot is purely chat-focused with no voice component?
Whether we obsess over or brush off language choices when afghanistan phone number list writing short messages or lengthier paragraphs, we practice language. The language and style guidelines will help designers understand commonly overlooked aspects of language, such as discourse markers (“oh”, “so”, or “well”) and how they influence how we interpret meaning.
With the bots modularity effort, we used punctuation and concise wording to convey enthusiasm. Think exclamation points, frequent “you” (second person) references, and using sentence fragments to indicate next steps or solicit information from the user.
This technique of centering language can also help answer another big question related to AI: With generative AI, how can you guarantee language consistency?