ustom functions can be defined and provided to Gemini models using the Function
Calling feature. The models do not directly invoke these functions, but instead
generate structured data output that specifies the function name and suggested
arguments. This output lets you write applications that take the structured
output and call external APIs, and the resulting API output can then be
incorporated into a further model prompt, allowing for more comprehensive query
responses. Function calling empowers users to interact with real-time
information and various services, such as databases, customer relationship
management systems, and document repositories, enhancing their ability to
provide relevant and contextual answers.

Recently, there has been considerable interest in large language models: machine learning systems which produce human-like text and dialogue. Applications of these systems have been plagued by persistent inaccuracies in their output; these are often called “AI hallucinations”. We argue that these falsehoods, and the overall activity of large language models, is better understood as bullshit in the sense explored by Frankfurt (On Bullshit, Princeton, 2005): the models are in an important way indifferent to the truth of their outputs. We distinguish two ways in which the models can be said to be bullshitters, and argue that they clearly meet at least one of these definitions. We further argue that describing AI misrepresentations as bullshit is both a more useful and more accurate way of predicting and discussing the behaviour of these systems.