Applying prompts to GenAI/LLMs

From Google Scholar to Project Ideas or Using AI to Map the Future of Your Research Generated as well by Google Gemini Have you ever looked at a researcher’s Google Scholar profile and felt overwhelmed? Between the long lists of citations and dense technical titles, "connecting the dots" of a decade-long career is a massive cognitive lift. Whether you are a student hunting for a dissertation topic or a professional scouting for a collaborator, understanding the trajectory of research is harder than reading the papers themselves. In my latest experiment, I used Google Gemini to see if it could bridge this gap. I gave it a specific challenge: Analyze my own research profile and design 10 compelling project ideas for a final-year Computer Science student. The Prompt: Turning Data into Direction The secret to a good AI output is giving it a clear "anchor." By providing a URL to a live dataset (my Scholar profile), I bypassed the need to copy-paste thousands of words....

Produced using Google Gemini In two previous posts I looked at using Generative AI to start producing  project ideas and refining one to be the start of the proposal, Previous blog posts GenAI Productivity: Ideas to project proposal 1 GenAI Productivity: Ideas to project proposal 2 One of the most significant cognitive hurdles for students is the transition from a high-level area of interest to a rigorously defined project specification. This "blank page" problem often leads to poorly scoped projects or a lack of alignment with current academic literature. Building on my previous experiments in prompt-mediated productivity, this post examines a structured workflow for using Large Language Models (LLMs)—specifically GPT-4o—to scaffold the development of a BSc Software Engineering project proposal. The Methodology: Multi-Stage Prompt Scaffolding Rather than requesting a single output, the workflow utilizes a recursive prompting strategy. This mirrors the iterative refinement pr...