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Does anyone else hate reading AI summaries of code? Code can be pithy, but at least its terse compared to prose. When you add how verbose LLMs can be, I often end up reading a paragraph to explain a few lines. Or the opposite happens where the summary skips important edge cases or criteria. "You're right, X also does Y. I missed that in my initial analysis," is much too common of a phrase. I like the idea of using LLMs to transform code into something more readable, and vice versa. I am not sure if meandering paragraphs and linear lists are the best targets.
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I wonder if a productive avenue might be "doing aspect-oriented programming in reverse": asking the LLM not to "summarize" the code per se, but rather to "clarify" it by transforming it into what a programming blog post would call a "toy example" of what the code is doing, by stripping out all the (non-semantic) error-handling, logging, metrics incrementing, etc — all the things that you might treat as their own "aspects" under AOP.
AI summaries are great for learning which parts of the codebase are load bearing.
I've found that I don't mind getting a lot of text back from an LLM, when I was the one who prompted it. I can easily enough let my eyes flit around in the text and figure out what I need to, and I expect that putting all that text in the context window will help with the rest of the conversation. It's when that text gets copied and pasted into a blog, or a PR, etc. that it really galls.
I don't like the default explanations. But I prompt for small code snippets with explanations of a problem and the solution. This is in the context of extending features, fixing bugs, reviewing new code etc. I still skim the code, but it's nice to have a somewhat thoughtful overview of the key points like database schema, API spec, algorithm or abstraction. It makes it easier to skim a large diff without feeling lost. It also quite often catches some weird choices that might slip through it not carefully reasoning about the code. For me it is hard to understand code I didn't write myself so I have landed on this workflow.
The "language model" aspect shows IMO, at least for someone who grew up with the material of 90s and 2000s where we describe things more in mechanical, engineering terms. When gpt4 landed it was somehow amazing to see the output, but yeah nowadays I keep skimming through the explanation. It's like an intermediate dev who has nothing else to do but create long sentences to describe what could be simpler.