What is one way to utilize Input Recommender in bot development?

Utilizing Input Recommender in bot development can be effectively achieved by enabling automated data sourcing for utterance generation. Input Recommender leverages patterns from existing data to generate various input options, enhancing the bot's ability to understand and respond appropriately to user queries. By automating the sourcing of data, the bot can create a more comprehensive set of utterances that reflect real user interactions, leading to improved accuracy and performance in understanding user intents.

This method is particularly advantageous as it allows the bot to evolve and adapt to changing user behaviors and language without requiring constant manual updates. It maximizes efficiency and ensures that the bot remains relevant in its responses by drawing from a broader set of interactions, thus providing a better user experience.

Other choices, such as manually inputting data for each session, rely heavily on human effort and are less efficient in dynamically adapting to user feedback. Relying solely on expert opinions does not harness the data-driven capabilities of a system like Input Recommender, which thrives on diverse user interactions for generating insights. Excluding customer service feedback would limit the bot's learning potential from real engagement scenarios, hence reducing its effectiveness.