Comment by bredren
14 hours ago
Cursor and other IDE modality solutions are interesting but train sloppy use of context.
From the extracted prompting Cursor is using:
> Each time the USER sends a message, we may automatically attach some information about their current state…edit history in their session so far, linter errors, and more. This information may or may not be relevant to the coding task, it is up for you to decide.
This is the context bloat that limits effectiveness of LLMs in solving very hard problems.
This particular .env example illustrates the low stakes type of problem cursor is great at solving but also lacks the complexity that will keep SWE’s employed.
Instead I suggest folks working with AI start at chat interface and work on editing conversations to keep clean contexts as they explore a truly challenging problem.
This often includes meeting and slack transcripts, internal docs, external content and code.
I’ve built a tool for surgical use of code called FileKitty: https://github.com/banagale/FileKitty and more recently slackprep: https://github.com/banagale/slackprep
That let a person be more intentional about what the problem they are trying to solve by only including information relevant to the problem.
I had this thought as well and find it a bit surprising. For my own agentic applications, I have found it necessary to carefully curate the context. Instead of including an instruction that we "may automatically attach", only include an instruction WHEN something is attached. Instead of "may or may not be relevant to the coding task, it is up for you to decide"; provide explicit instruction to consider the relevance and what to do when it is relevant and when it is not relevant. When the context is short, it doesn't matter as much, but when there is a difficult problem with long context length, fine tuned instructions make all the difference. Cursor may be keeping instructions more generic to take advantage of cached token pricing, but the phrasing does seem rather sloppy. This is all still relatively new, I'm sure both the models and the prompts will see a lot more change before things settle down.