Two months ago an email from Google landed in the author’s inbox, reminding him of a forgotten application. A screening call revealed two online interviews—technical and behavioral—within a week. With a background in telecom software and hobbyist game dev, he faced a steep algorithmic gap that needed rapid closure.

Instead of diving into textbooks, he turned to Gemini Pro, instructing the model to act as a private tutor. The LLM supplied conceptual hints and attack vectors for LeetCode problems, avoiding code output. Early wins included Best Time to Buy and Sell Stock and Valid Anagram, giving him confidence that simple loops and arrays could solve real‑world tasks.

On the following days he tightened the process: a 10‑15 minute timebox per problem, then a blind rewrite of the LLM’s solution in his own style. He tackled 30 problems, from Reverse Linked List to Climbing Stairs, discovering that tree traversals mirrored earlier GUI component searches and that BFS/DFS patterns were universally applicable.

By the end of the week, the author had bridged the gap between pragmatic software work and algorithmic thinking. The experience proved that an LLM can accelerate pattern recognition when paired with disciplined practice. The takeaway: structured, concept‑first learning beats rote problem solving and equips engineers for high‑stakes interviews.