A practical risk framework for large language model use in life science research
TL;DR
It is argued that effective prompt design is itself a primary risk management strategy, reducing the probability and severity of characteristic failures before verification is required, and introduced a structured framework for matching downstream verification effort to risk across dimensions of output verifiability, researcher expertise, and consequence of error.
Abstract
Responsible use of large language models (LLMs) in life science research demands a unified approach to risk, yet existing guidance treats prompting and verification as separate topics rather than as integrated components of a single risk management framework. This paper addresses that gap for life science researchers. We explain how LLM architecture produces both remarkable capabilities and characteristic failures including hallucination and sycophancy, with particular attention to vulnerabilities most relevant to life science workflows. We argue that effective prompt design is itself a primary risk management strategy, reducing the probability and severity of characteristic failures before verification is required, and introduce a structured framework for matching downstream verification effort to risk across dimensions of output verifiability, researcher expertise, and consequence of error. We apply both layers to common research tasks, including literature synthesis, code generation, writing assistance, statistical reasoning, and administrative work, with documentation practices and ethical obligations addressed throughout. Six supplementary guides extend each component into detailed workflows, worked examples, and reference checklists, and a companion open-source repository (https://github.com/SharptonLab/PromptLab) provides tested prompts and verification tools for immediate use. Together, these resources are designed to help researchers use these tools carefully and document how they did so.