The content describes how to automate an arborist's workflow, from risk assessment to final proposal, by eliminating manual processes and errors. The proposed solution involves a unified data pipeline that feeds an AI tool like ChatGPT to generate technical reports and customized client proposals.
The author developed an AI document pipeline to prevent hallucinations by moving away from free-text generation. It uses strict JSON schemas, domain-specific validation rules, and human-in-the-loop approval to catch errors before documents reach customers.
This post discusses the bottleneck of Chinese language data for LLM training and proposes a practical multi-source pipeline. It details how to scrape clean, structured data from key Chinese social platforms like Weibo, Bilibili, and Xiaohongshu to enrich training datasets.