The effectiveness of LLM-assisted research depends largely on whether a researcher’s own methods, procedures, and accumulated knowledge can be effectively invoked by the model. To solve this issue, the talk examines three levels of organization. Skills: packaging domain-specific research methods, together with their supporting corpora, into callable modules, with a worked example illustrating their structure and construction. Workflows: design elements including task decomposition, role separation, quality gates, and human checkpoints, drawing on the operating records of fully automated paper-generation systems such as Paper Factory and Project APE to discuss the limits of research automation. Knowledge bases: the content layer underlying the first two, where measured statistics from 56 public repositories inform writing conventions and maintenance practices that serve both the researcher and the AI as readers. The talk closes with a discussion of turning research needs into working software tools.<\/p>\n","protected":false},"featured_media":51220,"template":"","meta":{"_acf_changed":true},"etypes":[614],"tenses":[88],"locations":[106],"departments":[423],"class_list":["post-59619","events","type-events","status-publish","has-post-thumbnail","hentry","etypes-research-seminars","tenses-past","locations-on-campus","departments-marketing"],"acf":[],"yoast_head":"\n