This was an experimental development inspired by the fact that people at Vinnova seemed to have a hard time finding the information they were looking for in the currently available analytics tool. What if there was a virtual assistant that you could just tell which project population you are interested in and ask whatever you want to know and the assistant would then generate a chart that answers your question? The task here was to take the user query and turn it into Python code that reads the necessary information from a data table and that in the end generates a plotly chart that correctly answers the user’s question.

We were not able to finish this sub-project completely but the most promising approach based on experiments - comparing in-context learning and prompt-tuning - seemed to be few-shot prompting, where the few-shot examples were chosen based on semantic similarity with the user query and ordered so that the semantically most similar example to the user query was placed at the end of the prompt.