Programmers often benefit from the availability of concrete run-time data – examples – alongside abstract source code. However, programmers need to manually exercise the program to reach an interesting state or write code that reproducibly executes a functionality with concrete inputs to be able to observe concrete data.

In this work, we aim to automate this process by leveraging generative AI. We present a framework and a preliminary Smalltalk-based prototype that allows programmers to obtain and run examples for the currently viewed section of source code from a large language model.

Our approach demonstrates how locally-hosted LLMs can be fine-tuned and used for such a task with reasonable computational effort while minimizing common problems like hallucinations and out-of-date knowledge. The framework has direct applications in example-based live programming where it can suggest new examples, and in learning settings where novices need to know how certain functionality is being used.