The emergence of Large Language Models (LLMs), such as ChatGPT, has introduced a new set of tools to support software developers in solving programming tasks. However, our understanding of the interactions (i.e., prompts) between developers and ChatGPT that result in contributions to the codebase remains limited. To explore this limitation, we conducted a manual evaluation of 155 valid ChatGPT share links extracted from 139 merged Pull Requests (PRs), revealing the interactions between developers and reviewers with ChatGPT that led to merges into the main codebase. Our results produced a catalog of 14 types of ChatGPT requests categorized into four main groups. We found a significant number of requests involving code review and the implementation of code snippets based on specific tasks. Developers also sought to clarify doubts by requesting technical explanations or by asking for text refinements for their web pages. Furthermore, we verified that prompts involving code generation generally required more interactions to produce the desired answer compared to prompts requesting text review or technical information.