This is a clever use case for LLMs, but I'd be curious about the quality of signal you're extracting from Discord noise. How are you handling the challenge of distinguishing genuine feature requests from off-topic conversation or jokes? Are you doing any filtering/validation before feeding to the model, or relying on the LLM to handle that context-switching itself?
For now it relies on LLM itself, but you also can choose which kinds of items should be added to roadmap, like only features, or features + bugs, and AI will detect them, assign respective categories and notify owner.
So far the quality is really good, it can detect questions, general discussions from feature requests pretty well.
This is a clever use case for LLMs, but I'd be curious about the quality of signal you're extracting from Discord noise. How are you handling the challenge of distinguishing genuine feature requests from off-topic conversation or jokes? Are you doing any filtering/validation before feeding to the model, or relying on the LLM to handle that context-switching itself?
For now it relies on LLM itself, but you also can choose which kinds of items should be added to roadmap, like only features, or features + bugs, and AI will detect them, assign respective categories and notify owner.
So far the quality is really good, it can detect questions, general discussions from feature requests pretty well.