It's not that great at answering questions specific to my company's code base - like where do I find another API method that does Y. However, we have a lot of guides internally which are usually scattered around in github readmes and usually those are what I fallback to when trying to find an answer. These github readmes and be quite long though and it can feel like a lot of manual work to scan through them, that's where I'll usually use this extension Rocky AI to quickly ask a question given the context of a webpage (helps avoid reading through the entire thing manually). After quickly browsing 2-3 and I'll have what I need.

https://chromewebstore.google.com/detail/rocky-ai/fdjoklehji...

They don't work well at all for us.

We have a relatively large Rust codebase and I would describe the LLM's as actively harmful at making agentic-style, self-piloted changes.

Very pointed context management with split screens and explicit inclusion of relevant files in the context is sometimes useful though.

My biggest gripe is that AI can't just generate a new SQL migration and CRUD routes for an additional resource we want to add without issue. I always have to punt and end up doing it myself.

Also to add some more context:

- I've found LLM codegen working very well with standard React + TS code

- It sucks when using less knows languages or less popular frameworks (I tried reverse engineering github copilot lua code from the neovim extension in one instance and it really didn't work well)

I'd be curious to hear people's experience here.