Skip to main content

Using AI to Reinvent My Résumé and Try to Land an Interview



Creating a résumé is a tedious job to most. It's hard, time consuming and might even be the cause for rejection-if you don't know what you're doing. Fortunately, there are AI tools out there that created to assist, us humans, in generating résumé. It save's time, effort and you get higher chance of being hired. 

But what if you're transitioning to an entirely different role? You don't have experience, no educational background to back it up. and no portfolio to show. What do you do? You come up with something creative. You come up with some that has never been done before. And, just wow them... or at least try.

I was messaged in LinkedIn for a position that I was eyeing for in years. The HR guy reached out and we scheduled a call interview. We talked for more than half an hour and I was enlightened that my résumé is lack-luster. I was highly considered but the résumé is not at par because I have no job experience on AI, the certifications we're not included, and I don't have any portfolio to show. 

So I got creative.

I collaborated with ChatGPT in creating a project proposal fit for the position. If photographers use portfolio to showcase their shot and musicians use recordings to demo their song, it's only fitting in my opinion to generate a document such as a project proposal to exhibit my prowess in prompt engineering.

Everything's submitted. Now let's see what will happen.


Labels:

Popular

Prompt Analysis Using First-Principles Thinking (FPT)

Instead of memorizing existing prompt patterns, let’s break down Prompt Analysis from First-Principles Thinking (FPT) —understanding what makes a prompt effective at its core and how to optimize it for better AI responses. Step 1: What is a Prompt? At its most fundamental level, a prompt is just: An input instruction → What you ask the AI to do. Context or constraints → Additional details that guide the response. Expected output format → Defining how the AI should structure its answer. A well-designed prompt maximizes relevance, clarity, and accuracy while minimizing misunderstandings. Step 2: Why Do Prompts Fail? Prompts fail when: ❌ Ambiguity exists → The model doesn’t know what’s truly being asked. ❌ Lack of context → Missing background information leads to weak responses. ❌ Overloaded instructions → Too many requirements confuse the AI. ❌ Vague output expectations → No clear structure is provided. ❌ Incorrect assumptions about AI behavior → The prompt d...
Read more

Understanding Large Language Models (LLMs) Using First-Principles Thinking

Instead of memorizing AI jargon, let’s break down Large Language Models (LLMs) from first principles —starting with the most fundamental questions and building up from there. Step 1: What is Intelligence? Before we talk about AI, let’s define intelligence at the most basic level: Intelligence is the ability to understand, learn, and generate meaningful responses based on patterns. Humans do this by processing language, recognizing patterns, and forming logical connections. Now, let’s apply this to machines. Step 2: Can Machines Imitate Intelligence? If intelligence is about recognizing patterns and generating responses, then in theory, a machine can simulate intelligence by: Storing and processing vast amounts of text. Finding statistical patterns in language. Predicting what comes next based on probability. This leads us to the core function of LLMs : They don’t think like humans, but they generate human-like text by learning from data. Step 3: How Do LLMs Wor...
Read more

Contextual Stratification - Chapter 25: AI and Technology

  Machines at Boundaries In 2016, AlphaGo defeated the world champion at Go, a game so complex that brute-force computation seemed impossible. The victory felt momentous: machines mastering domains requiring intuition, pattern recognition, strategic depth. Then researchers tried applying the same system to StarCraft, a real-time strategy game. It struggled. Same underlying architecture, different domain; and the framework that dominated Go couldn't transfer. This isn't a flaw in AlphaGo. It's a demonstration of contextual stratification in artificial systems. The AI learned F_Go at λ_board-game with M_Go (measurable game states, valid moves, winning positions). That framework produced brilliant Q_Go (optimal strategies, creative plays). But F_Go doesn't apply to F_StarCraft at λ_real-time with different M_StarCraft. The boundary between frameworks isn't crossable by mere scaling. It requires different architecture, different learning, different framework. AI system...
Read more