Launch HN: GradientJ (YC W23) – Build NLP Applications Faster with LLMs

Hey HN, we’re Daniel and Oscar, founders of GradientJ ( https://gradientj.com ), a web application that helps teams develop, test, and monitor natural language processing (NLP) applications using large language models (LLMs). Before GradientJ, we’d been building NLP applications for 4 years, using transformer models like BERT. With the advent of LLMs and their zero-shot/few-shot capabilities, we saw the NLP dev cycle get flipped on its head. Rather than having to hire an army of data labelers and data scientists to fine-tune a BERT model for your use case, engineers can now use LLMs, like GPT-4, to build NLP endpoints in minutes. As powerful as this is, the problem becomes that without appropriate tools for version control, regression testing, and ongoing maintenance like monitoring and A/B testing, managing these models is a pain. Because the data being evaluated is often fuzzy, developers either have to build complex text processing regex pipelines or manually eva