Large language models (LLMs) like ChatGPT have gained much popularity in recent times, with businesses exploring how artificial intelligence can improve their operations. However, relying on the output of these models for mission-critical decisions is risky. While LLMs can search vast amounts of data to generate conversational and human-like responses, their knowledge is limited by the data underpinning them. Moreover, LLMs like ChatGPT have been trained on internet text data, including misinformation and personal biases, making them susceptible to fabricating evidence. To maximize the value of LLMs, businesses need to add their own proprietary data into the mix. Integration of all relevant internal and external data pipelines is essential to improve the quality, accuracy, and reliability of LLM outputs. While building LLMs requires expert data science skills, companies can invest in laying the groundwork for data stewardship, making their internal data easy to work with and adopting data management principles such as FAIR.
ChatGPT, the subject of this article, is a language model platform that leverages open-source technology to deliver its services.
Sam Altman, the CEO of OpenAI, is highlighted in the article as he provides a warning around the limitations of technologies like ChatGPT.
