I think an even more interesting use case for semgrep, and also LSP or something like LSP, is querying for exactly what an AI needs to know to fix a specific problem.
Unlike humans, LLMs have no memory, so they can't just learn where things are in the code by remembering the work they did in the past. In a way, they need to re-learn the relevant parts of your codebase from scratch on every change, always keeping context window limitations in mind.
Humans learn by scrolling and clicking around and remembering what's important to them; LLMs can't do that. We try to give them autogenerated codebase maps and tools that can inject specific files into the context window, but that doesn't seem to be nearly enough. Semantic queries look like a much better idea.
I thought you couldn't really teach an LLM how to use something like that effectively, as that's not how humans work and there's no data to train on, but the recent breakthroughs with RL made me change my mind.
Unlike humans, LLMs have no memory, so they can't just learn where things are in the code by remembering the work they did in the past. In a way, they need to re-learn the relevant parts of your codebase from scratch on every change, always keeping context window limitations in mind.
Humans learn by scrolling and clicking around and remembering what's important to them; LLMs can't do that. We try to give them autogenerated codebase maps and tools that can inject specific files into the context window, but that doesn't seem to be nearly enough. Semantic queries look like a much better idea.
I thought you couldn't really teach an LLM how to use something like that effectively, as that's not how humans work and there's no data to train on, but the recent breakthroughs with RL made me change my mind.