> My experience has been similar, and it's why I prefer to keep LLMs separate from my code base.
I'm curious how you're managing this - is it primarily by inputting code snippets or abstract context into something like a Claude or ChatGPT?
I found for myself that I usually was bad at providing sufficient context when trying to work with the LLM separately from the codebase, but also might lack the technical background or appropriate workflow.
> ... is it primarily by inputting code snippets or abstract context into something like a Claude or ChatGPT?
I usually provide the initial context by describing the app that I'm working on (language, framework, etc) as well as the feature I want to build, and then add the files (either snippets or upload) that are relevant to build the feature (any includes or other files it will be integrating with).
This keeps the chat context focused, and the LLM still has access to the code it needs to build out the feature without having access to the full code base. If it needs more context (sometimes I'll ask the LLM if they want access to other files), I'll provide additional code until it feels like it has enough to work with to provide a solution.
It's a little tedious, but once I have the context set up, it works well to provide solutions that are (mostly) bug free and integrate well with the rest of my code.
I primarily work with Perplexity Pro so that I have access to and can switch between all pro level models (Claude, ChatGPT, Grok, etc) plus Google search results for the most up-to-date information.
I'm curious how you're managing this - is it primarily by inputting code snippets or abstract context into something like a Claude or ChatGPT?
I found for myself that I usually was bad at providing sufficient context when trying to work with the LLM separately from the codebase, but also might lack the technical background or appropriate workflow.