This comprehensive implementation guide accompanies the framework described in "Build expertise first: why PhD training must sequence AI use after foundational skill development" (Krishnan, 2026, DOI:10.5281/zenodo.18649847). While the article presents the intellectual foundation—explaining why generative AI poses unique challenges during training and what principles should guide its use—this guide provides the practical implementation details: how to apply the "expertise before augmentation" framework across specific research tasks. How to use these resources together: The article establishes the core principles: the distinction between mechanical and cognitive automation, the verification paradox, the developmental sequencing framework, and the stakes for individuals and science. It's designed for understanding the rationale and making the case for thoughtful AI integration. This guide translates those principles into actionable protocols for common PhD training activities: computational data analysis, literature review and synthesis, manuscript and proposal writing, figure generation, and communication practice. It's designed for day-to-day decision-making by trainees, mentors, and program administrators. Recommended use: Read the article first to understand the framework, then use this guide as a reference document for implementing policies, advising trainees, or making personal decisions about AI use during research training. Citation: Krishnan, A. (2026). Expertise before augmentation: a practical guide to using generative AI during research training. Zenodo. 10.5281/zenodo.18452319 License: This work is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). You are free to share and adapt this material for any purpose, even commercially, as long as you give appropriate credit, provide a link to the license, and indicate if changes were made. Version, Feedback, and Updates: This is Version 1.0 (February 2026). If you're using an adapted version, please note both the original source and your modifications. For example: "Adapted from Krishnan (2026), modified to include [specific changes]." The last page of the guide contains a section on "How to adapt this guide for your institution". If you adapt this guide and would like to share your version, or if you have suggestions for improving future versions, please contact the author.