Yes, building AI chat is still hard (getlago.com)

In a recent reflection on developing AI chat solutions, Anh-Tho Chuong details the challenges faced while building advanced AI agents, particularly in the context of billing workflows. The team at Lago recognized early on that simply creating a generic chatbot was insufficient; instead, they aimed for functional agents capable of interacting with APIs and executing sensitive financial tasks. This decision highlighted the importance of caution, as operational errors in billing could lead to significant trust issues and financial consequences. They implemented robust safety measures, including strict role-based access controls and confirmation prompts for critical actions, to mitigate risks of AI hallucinations—where the AI might make incorrect or harmful decisions. This nuanced approach to AI chat development underscores a critical takeaway for the AI/ML community: building effective AI tools requires more than leveraging existing technologies; it necessitates a deep understanding of the specific use cases and potential repercussions. By establishing distinct assistants tailored to different user needs within the billing context, Lago aims to streamline functionality while ensuring safety and reliability. This case serves as a reminder that the pursuit of AI innovation must be balanced with a commitment to accountability and precision, particularly in sensitive areas like finance.