  In many legal processes being able to action on the concrete implication of a
legal question can be valuable to automating human review or signalling certain
conditions (e.g., alerts around automatic renewal). To support such tasks, we
present a form of legal question answering that seeks to return one (or more)
fixed answers for a question about a contract clause. After showing that
unstructured generative question answering can have questionable outcomes for
such a task, we discuss our exploration methodology for legal question
answering prompts using OpenAI's \textit{GPT-3.5-Turbo} and provide a summary
of insights.
  Using insights gleaned from our qualitative experiences, we compare our
proposed template prompts against a common semantic matching approach and find
that our prompt templates are far more accurate despite being less reliable in
the exact response return. With some additional tweaks to prompts and the use
of in-context learning, we are able to further improve the performance of our
proposed strategy while maximizing the reliability of responses as best we can.
