WEBVTT

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Here is the complete, production-ready narration script for **M11-L01: The AI

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Prompt Library**, designed to hit the 12-15 minute runtime with a

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conversational, coaching tone. *

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(Visual: Instructor looking directly at the camera, energetic and serious.)

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Instructor: What if I told you that every interview question you will ever

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face... has already been asked? And what if I told you that an AI could simulate

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fifty practice interviews before your real one, personalized to your exact role,

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your target company, and the exact personality of your interviewer?

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Welcome to Module 11, Lesson 1: The AI Prompt Library.

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If you’ve been following the GHFE system, you’ve built your foundation,

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constructed a killer resume using the CAR Method, and optimized your LinkedIn

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profile. But today, we are giving you a massive unfair advantage. Today, I'm

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handing over the keys to our highly engineered, fill-in-the-blank AI Prompt

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Library.

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Here’s the reality: everyone is using AI for their job search right now. But

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most people use it like a fancy search engine. They type a vague question, and

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they get a generic, robotic answer.

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A true strategist—a GHFE operator—uses AI as a highly specialized collaborator.

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Today, I'm going to show you how to engineer your career with the precision of a

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developer shipping code. We're going to cover the prompt engineering mindset,

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the Interview Simulator, the Semantic Gap Analyzer, our 7-category prompt

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library, and finally, a critical test to make sure your AI-generated content

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doesn’t cost you the job . Let's dive in.

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(Visual: Split screen showing a BAD prompt vs. a GOOD prompt.)

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Instructor: Let me show you what the difference between an amateur and a pro

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looks like. An amateur types: "Help me prepare for my interview."

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The AI gives them generic trivia.

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A pro types: *"You are a VP of Engineering at a Series-B fintech company. You're

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interviewing a Senior Backend Developer. You value systems thinking and

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ownership. Ask me 5 behavioral questions that probe for these qualities. After

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each of my answers, rate it 1-10 and explain what was missing."*

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See the difference? The bad prompt gets you a Wikipedia summary. The good prompt

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puts a real person across the table from you.

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The secret behind that good prompt is structure. Every prompt in our library

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follows the same architecture, which I call the CRTCO Framework:

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* Context: What is the background situation? (e.g., "I am applying for a

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Series-B fintech startup.")

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* Role: Who is the AI pretending to be? (e.g., "You are the VP of

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Engineering.")

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* Task: What exactly do you need it to do? (e.g., "Ask me 5 behavioral

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questions.")

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* Constraints: What are the rules it must follow? (e.g., "Wait for my answer

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before asking the next question. Do not break character.")

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* Output Format: How should it deliver the information? (e.g., "Rate my

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answer 1-10 and provide a one-sentence critique.")

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When you use CRTCO, you stop asking the AI for favors and start giving it an

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operating system.

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**(Visual: Step-by-step prompt flow on screen: JD -> Interviewer Profiles ->

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Simulation -> 3 Rounds -> Debrief.)**

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Instructor: Now, let’s apply this framework to build your personal interview

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simulator.

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As we discussed in Module 6, asynchronous video interviews—like HireVue—are

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becoming incredibly common. To beat them, we use the **HireVue Simulator

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Prompt**. This prompt literally forces the Large Language Model to behave as an

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automated interview platform.

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Here is the exact 5-step prompt sequence to set this up:

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1. Step 1: Feed the Job Description. You paste the target JD so the AI knows

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the exact core skills and success metrics required.

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2. Step 2: Feed the Interviewer Profiles. Paste the LinkedIn summaries of

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three people you are likely to interview with.

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3. Step 3: Activate the Simulation. Use our fill-in-the-blank prompt to tell

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the AI to adopt the persona of the interviewer.

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4. Step 4: Run 3 Rounds. We simulate a Screening round with HR, a Technical

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round, and a Leadership round. 5.  Step 5: Debrief and Score.

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Imagine you're going for a Data Analyst role at a healthcare startup. You run

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this prompt, and the AI asks you a tough question. You type or dictate your

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STAR-method answer. The AI then instantly gives you a score out of 10 and shows

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you exactly what a 9-out-of-10 answer would have sounded like.

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If you run this simulation 10 times, by the time interview day arrives, you’ve

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already had 10 practice runs with a bot that thinks exactly like your actual

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human interviewer. It’s a completely unfair advantage.

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**(Visual: Side-by-side resume vs. Job Description with hidden gaps

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highlighted.)**

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Instructor: Next, I want to introduce you to my absolute favorite tool in

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the entire course: The Semantic Gap Analyzer.

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Most people know to look for missing keywords when applying for a job. But the

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Semantic Gap Analyzer goes much deeper. It compares your resume against the

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target job description to find what I call "inferred skill gaps". These are the

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discrepancies between the skills the employer implicitly requires and the ones

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you are actually presenting.

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When you run this prompt, it identifies four things:

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1.  Exact Keyword Mismatches. The obvious stuff.

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2. Tonal Misalignments. Are you sounding like a middle-manager when they

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want a scrappy startup operator?

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3. Missing Soft Signals. Does your resume show cross-functional

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collaboration, or just isolated technical work?

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4. Implicit Seniority Markers. Are you using language that sounds too junior

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for a VP role?

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Here is the magic of the Semantic Gap Analyzer: It doesn't just tell you to "add

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a keyword." It tells you to strategically reframe your experience. It might

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say, "Don't just add the word 'strategy'; reframe your third bullet point to

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signal strategic thinking by showing how you tied project outcomes to company

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revenue.

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This prompt bridges the gap between what you have done and what the Applicant

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Tracking System—and the hiring manager—needs to see.

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**(Visual: 7 cards appearing on screen with dark background and neon accent

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colors.)**

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Instructor: The Interview Simulator and the Semantic Gap Analyzer are just

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the beginning. Below this video, you will find the complete GHFE AI Prompt

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Library worksheet. It contains highly engineered prompts broken down into 7

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specific categories:

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1. Resume Optimization: The Achievement Miner. This prompt digs into your

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vague job duties and translates them into CAR-method (Challenge-Action-Result)

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bullet points loaded with hard metrics.

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2. Cover Letter: The Problem-Solution-Proof Generator. This writes a

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250-word cover letter that hooks the reader, articulates the company's "Bleeding

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Neck" problem, and positions you as the exact solution.

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3. LinkedIn: The Profile Optimizer. This prompt rewrites your headline and

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summary to exploit LinkedIn's Interest Graph algorithm, maximizing your

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discoverability to recruiters.

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4. Company Research: The Intelligence Briefing. Feed this prompt a company

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name, and it extracts their mission, recent challenges, and potential interview

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talking points to build your 5-Layer Prep System.

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5.  Interview Prep: The Simulator. (Which we just covered).

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6. Networking: The Outreach Composer. This prompt generates short, specific,

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and flattering outreach messages and 3-touch follow-up sequences for your

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informational interviews.

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7. Negotiation: The Market Rate Triangulator. Feed it your competing offers

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and market data, and it will script a collaborative, data-backed 4-step

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negotiation conversation to ensure you get your Target or Stretch number.

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These prompts do the heavy lifting for you. But, and this is a massive "but",

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you cannot just copy and paste the outputs blindly. Which brings us to our

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final, critical step.

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**(Visual: Text on screen - "The AI Neutralization Effect" transitioning to "The

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Copy-Paste Detection Test".)**

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Instructor: Because AI is so accessible, the baseline quality of resumes and

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cover letters has skyrocketed. Everyone sounds polished. But because everyone is

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using the same AI models, every application is starting to look and sound

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identical. I call this the AI Neutralization Effect.

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If you let AI write 100% of your materials, the AI will "smooth out" all the

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messy, non-linear, unique traits that make you a human being.

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To beat this, we use the 10% Human Injection Framework. You let the AI

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handle 90% of the structure, grammar, and keyword optimization. But you must

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manually inject 10% of unmistakable humanity—a specific anecdote, a contrarian

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insight, or a moment of strategic vulnerability.

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Before you ever submit a resume or hit "send" on an AI-generated message, you

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must run it through the Copy-Paste Detection Test.

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Here is how it works: Take any paragraph from your resume or cover letter and

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ask yourself: *"Could any other person with my job title have written this exact

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sentence?"*

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If the answer is yes, it fails the test. It is neutralized. You must rewrite it

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by injecting a human detail—a specific metric, an inside joke from your team, or

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an exact quote from a client. The future of career success isn't just about who

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uses AI the best. It's about who remains the most human while using AI the

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best. That 10% is your competitive moat.

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(Visual: Instructor pointing to the camera with a confident smile.)

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Instructor: Alright, here is your Coach's Challenge for this lesson. I want

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you to download the AI Prompt Library worksheet right now.

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Take your current resume and the job description for your top target role, and

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run them through the Semantic Gap Analyzer. I want you to log at least 5 hidden

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gap findings, apply the AI's suggested fixes, and then run it through the

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Copy-Paste Detection Test to make sure it still sounds like you.

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The tools in this library aren't magic—they are engineering. When you use them

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correctly, you stop playing the volume game and start playing the precision

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game.

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Do the work, run the prompts, and I’ll see you in the next lesson where we'll

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dive even deeper into overcoming the AI Neutralization Effect. Let's get to

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work. (Visual: GHFE Logo animation & closing music.)