How have you been able to manage the issue of unreliability with the volumes of data you’re dealing with? Is the kind of data which you’re dealing with less likely to be unreliable since it is of a kind the LLM is more likely to process correctly?

The same way for any other information resource like Wikipedia or some random Reddit post: trust but verify. Always review the code, point out mistakes, call out potential edge cases. Especially with newer thinking models, the hallucinations are minimal. It's mostly just miscommunication in the request, which you can detect in the Thinking stream, stop, and re-correct. Rubberducking makes you better at communicating ideas in general, and providing enough context for the request is everything.

A lot of it has to do with the type of model you're using, too, and having a decent global rules file tailored to how you want it to respond. If you don't like how the model is responding, try out another one. If it's some repeat mistake it makes, put it in a global rules file, or ask it to make a permanent memory.

Claude Opus does well at work, but is rather expensive for home use. I use Kimi reasoning models in Kagi for searching questions, and Qwen/GLM hybrid models for local use. It takes a bit of setup and tweaking to get the local stuff working, but LLMs are good at knowing how their own models work, so I just had Kimi help me out with some of the harder troubleshooting.