  The widespread adoption of large language models (LLMs), such as OpenAI's
ChatGPT, could revolutionize various industries, including geotechnical
engineering. However, GPT models can sometimes generate plausible-sounding but
false outputs, leading to hallucinations. In this article, we discuss the
importance of prompt engineering in mitigating these risks and harnessing the
full potential of GPT for geotechnical applications. We explore the challenges
and pitfalls associated with LLMs and highlight the role of context in ensuring
accurate and valuable responses. Furthermore, we examine the development of
context-specific search engines and the potential of LLMs to become a natural
interface for complex tasks, such as data analysis and design. We also develop
a unified interface using natural language to handle complex geotechnical
engineering tasks and data analysis. By integrating GPT into geotechnical
engineering workflows, professionals can streamline their work and develop
sustainable and resilient infrastructure systems for the future.
