Software maintenance constitutes a substantial portion of the total lifetime costs of software, with a significant portion attributed to code comprehension. Software comprehension is eased by documentation such as comments that summarize and explain code. We present ComCat , an approach to automate comment generation by augmenting Large Language Models (LLMs) with expertise-guided context to target the annotation of source code with comments that improve comprehension. Our approach enables the selection of the most relevant and informative comments for a given snippet or file containing source code. We develop the ComCat pipeline to comment C/C++ files by (1) automatically identifying suitable locations in which to place comments, (2) predicting the most helpful type of comment for each location, and (3) generating a comment based on the selected location and comment type. In a human subject evaluation, we demonstrate that ComCat -generated comments significantly improve developer code comprehension across three indicative software engineering tasks by up to 13% for 80% of participants. In addition, we demonstrate that ComCat -generated comments are at least as accurate and readable as human-generated comments and are preferred over standard ChatGPT-generated comments for up to 92% of snippets of code. Furthermore, we develop and release a dataset containing source code snippets, human-written comments, and human-annotated comment categories. ComCat leverages LLMs to offer a significant improvement in code comprehension across a variety of human software engineering tasks.