In this Part 2, I focus on the part of literature reviews that usually wastes the most time: citations and references. The goal is simple—stop manually hunting, formatting, and re-formatting sources, and instead build a fast workflow where AI helps you collect papers, extract the right metadata, and turn it into clean, usable references for your writing. I also show how I think about moving from “finding papers” to “actually writing,” so your literature review doesn’t become a copy-paste dump. I walk through how I use AI-assisted research tools (the same ecosystem I always test—ChatGPT-style prompting plus paper databases and reference managers) to speed up citation capture, keep sources organized, and reduce errors when you’re building a bibliography. If you’re writing proposals, research papers, or even a presentation based on papers, this workflow helps you go from scattered PDFs to a structured reference list quickly. The main takeaway: your literature review gets dramatically faster when you treat citations as a pipeline—search → verify → store → cite—rather than a last-minute formatting problem.