Day 2 – Standup on my side hustle (twitter.com)

In a recent update, a developer outlined their approach to enhancing tender matching using large language models (LLMs). The plan involves a two-step process: first, filtering tenders through basic, cost-effective LLMs to identify those that meet specific criteria, followed by applying more advanced LLMs to predict which tenders a company is most likely to win based on the filtered list. This method harnesses the power of AI to streamline the tender selection process and improve win rates for companies. Significantly, this project showcases the practical application of LLMs in a business context, illustrating how AI can be leveraged to make data-driven decisions in competitive bidding environments. By scraping tenders from sources like FTS and CT services and optimizing them through hierarchical filtering, the developer aims to create a more efficient and targeted approach to tender assignments. The use of both inexpensive and sophisticated models highlights a strategic use of resources, potentially benefiting companies looking to enhance their tender success rates.