This was my approach when using agents to analyze HVAC IoT data doing anomaly detection / investigations and it similarly worked very well. Mix that with some context like install location, geographic features with some context / info on seasonality (like ASHRAE values for the regions), and some classification like (residential / commercial), the bot was quite able to deliver actual insights into problems vs creating a bunch of excess noise.
We also mixed in some GSA (https://arxiv.org/abs/2503.04104) steps during the analysis in the sub agents to further reduce hallucinations
Glad to hear this. I actually went down this path based off of guidance from multiple LLMs (Anthropic, OpenAI, etc.), so I wasn't sure if it was just some kind of weird hallucination they all had or if they were regurgitating a very small amount of knowledge on this topic, because it was kinda hard to find stories where people had success with these strategies. Thank you for the link to the paper. I will definitely be reading it.
We also mixed in some GSA (https://arxiv.org/abs/2503.04104) steps during the analysis in the sub agents to further reduce hallucinations