Strengthen your research workflow with generative AI
Stop guessing at prompts. Use research-ready templates that guide Microsoft Copilot toward clearer reasoning, better drafts, and transparent methods—so your work is faster, sharper, and credible. Darcy Ogden, Ph. D., leads the academic researcher programs for Microsoft Global Skilling. A computational scientist and former professor of geophysics, Dr. Ogden has a passion for teaching and using new technology to accelerate research. Guidance on using generative AI in research often lands at the extremes; it’s either overly optimistic or far too cautious. Most researchers, students, and professionals working with data or analysis know that the reality sits somewhere in between: all models are useful but fallible tools. As researchers, we ask: What was this model designed to do? Where does it perform well? Where does it fall short? What assumptions are we making when we use this model? Those same questions apply to generative AI. Understanding how these systems work and how they shape the outputs you receive can help you decide when to rely on them and when to adjust. We’re all trying to figure out how best to work with generative AI, and there’s no simple, universal answer. But, in many cases, the work of research itself creates opportunities to apply generative AI thoughtfully and effectively. Explore our new guide for researchers As part of the latest Microsoft efforts to support graduate students, postdocs, and faculty aiming to use generative AI for research, we’re happy to share a new learning resource, The Academic Researcher's Guide to Generative AI. In this guide, we bring together recent insights and practical frameworks for considering generative AI as a research instrument. The guide’s purpose is to support researchers in asking well‑formed questions about the tools they use and in reflecting on the role that those tools play in research processes. Bring generative AI into your research methodology This new guide provides research-aligned approaches to prompting in Microsoft 365 Copilot Chat, along with frameworks for prompt development, testing, and documentation. Further, it includes ready-to-adapt prompting use cases for research scenarios. The following brief examples reflect the kinds of tasks that these prompts support: Research synthesis. Summarize the key arguments across these sources and note where the evidence conflicts. Writing support. Rewrite this paragraph for clarity and precision while keeping the original meaning. Data analysis. Explain the assumptions behind this statistical method and list situations where it may fail. We’ve also included guidance on crafting quality prompts in Copilot, with techniques that can help reduce ambiguity and surface the reasoning behind responses. These approaches for prompting can deliver tailored, well-structured outputs suited for research purposes. The following examples highlight the types of instructions that researchers can use to make the most of Copilot prompts: Surface assumptions. State assumptions and show reasoning before providing the final answer. Limit sources. Use only the attached sources and flag any gaps or uncertainties in the evidence. Structure responses. Follow this structure: Context → key points → limitations → questions to be considered next. This guide treats the use of generative AI like other models or tools you use in your research. Like them, generative AI has no native understanding of fields of study, datasets, or research constraints. The guide introduces an approach to using generative AI as a visible, documentable part of academic research. It treats interactions with Copilot as part of your methodology: something to record, review, and refine as you move through your research. Put the guide to work As generative AI becomes more common across academic and professional environments, the question is no longer whether to use it but how to use it well. As the models grow more capable, the challenge is how to use them in ways that support learning, integrity, and transparency. We developed this guide to help researchers and students engage these tools in ways that strengthen, rather than diminish learning and scholarly judgment. We invite you to read The Academic Researcher's Guide to Generative AI. Use it as a starting point, adapt the frameworks to your own discipline and workflow, and contribute feedback about the guide so that we can continue to evolve this resource alongside the field itself.
