The widespread adoption of large language models (LLMs), such as OpenAI's ChatGPT, could revolutionized 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.
翻译：广泛采用大型语言模型（LLM），如OpenAI的ChatGPT，可以革命各行各业，包括岩土工程。然而，GPT模型有时会生成听起来合理但虚假的输出，导致幻觉。本文讨论了及时工程对缓解这些风险和发挥GPT在岩土应用中的全部潜力的重要性。我们探讨了LLM的挑战和陷阱，并强调了上下文在确保准确和有价值的回应方面的作用。此外，我们研究了开发上下文特定搜索引擎的发展方向，并探讨了LLM成为复杂任务（如数据分析和设计）的自然界面的潜力。我们还开发了一个使用自然语言处理复杂的岩土工程任务和数据分析的统一界面。通过将GPT整合到岩土工程工作流程中，专业人士可以简化他们的工作，并为未来开发可持续和弹性下的基础设施系统。
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