  With the success of large language models (LLMs) of code and their use as
code assistants (e.g. Codex used in GitHub Copilot), techniques for introducing
domain-specific knowledge in the prompt design process become important. In
this work, we propose a framework called Repo-Level Prompt Generator that
learns to generate example-specific prompts using prompt proposals. The prompt
proposals take context from the entire repository, thereby incorporating both
the structure of the repository and the context from other relevant files (e.g.
imports, parent class files). Our technique doesn't require any access to the
weights of the LLM, making it applicable in cases where we only have black-box
access to the LLM. We conduct experiments on the task of single-line
code-autocompletion using code repositories taken from Google Code archives. We
demonstrate that an oracle constructed from our prompt proposals gives a
remarkably high relative improvement of 36% over Codex, showing the quality of
these proposals. Further, we show that when we train a model to predict a
prompt proposal, we can achieve significant performance gains over Codex and
other baselines. We release our code, data, and trained checkpoints at:
\url{https://github.com/shrivastavadisha/repo_level_prompt_generation}.
