  Continuous evolution in modern software often causes documentation,
tutorials, and examples to be out of sync with changing interfaces and
frameworks. Relying on outdated documentation and examples can lead programs to
fail or be less efficient or even less secure. In response, programmers need to
regularly turn to other resources on the web such as StackOverflow for examples
to guide them in writing software. We recognize that this inconvenient,
error-prone, and expensive process can be improved by using machine learning
applied to software usage data. In this paper, we present our practical system
which uses machine learning on large-scale telemetry data and documentation
corpora, generating appropriate and complex examples that can be used to
improve documentation. We discuss both feature-based and transformer-based
machine learning approaches and demonstrate that our system achieves 100%
coverage for the used functionalities in the product, providing up-to-date
examples upon every release and reduces the numbers of PRs submitted by
software owners writing and editing documentation by >68%. We also share
valuable lessons learnt during the 3 years that our production quality system
has been deployed for Azure Cloud Command Line Interface (Azure CLI).
