Here is the complete, production-ready narration script for **M11-L01: The AI
Prompt Library**, designed to hit the 12-15 minute runtime with a
conversational, coaching tone. *
(Visual: Instructor looking directly at the camera, energetic and serious.)
Instructor: What if I told you that every interview question you will ever
face... has already been asked? And what if I told you that an AI could simulate
fifty practice interviews before your real one, personalized to your exact role,
your target company, and the exact personality of your interviewer?
Welcome to Module 11, Lesson 1: The AI Prompt Library.
If you’ve been following the GHFE system, you’ve built your foundation,
constructed a killer resume using the CAR Method, and optimized your LinkedIn
profile. But today, we are giving you a massive unfair advantage. Today, I'm
handing over the keys to our highly engineered, fill-in-the-blank AI Prompt
Library.
Here’s the reality: everyone is using AI for their job search right now. But
most people use it like a fancy search engine. They type a vague question, and
they get a generic, robotic answer.
A true strategist—a GHFE operator—uses AI as a highly specialized collaborator.
Today, I'm going to show you how to engineer your career with the precision of a
developer shipping code. We're going to cover the prompt engineering mindset,
the Interview Simulator, the Semantic Gap Analyzer, our 7-category prompt
library, and finally, a critical test to make sure your AI-generated content
doesn’t cost you the job . Let's dive in.
(Visual: Split screen showing a BAD prompt vs. a GOOD prompt.)
Instructor: Let me show you what the difference between an amateur and a pro
looks like. An amateur types: "Help me prepare for my interview."
The AI gives them generic trivia.
A pro types: *"You are a VP of Engineering at a Series-B fintech company. You're
interviewing a Senior Backend Developer. You value systems thinking and
ownership. Ask me 5 behavioral questions that probe for these qualities. After
each of my answers, rate it 1-10 and explain what was missing."*
See the difference? The bad prompt gets you a Wikipedia summary. The good prompt
puts a real person across the table from you.
The secret behind that good prompt is structure. Every prompt in our library
follows the same architecture, which I call the CRTCO Framework:
* Context: What is the background situation? (e.g., "I am applying for a
Series-B fintech startup.")
* Role: Who is the AI pretending to be? (e.g., "You are the VP of
Engineering.")
* Task: What exactly do you need it to do? (e.g., "Ask me 5 behavioral
questions.")
* Constraints: What are the rules it must follow? (e.g., "Wait for my answer
before asking the next question. Do not break character.")
* Output Format: How should it deliver the information? (e.g., "Rate my
answer 1-10 and provide a one-sentence critique.")
When you use CRTCO, you stop asking the AI for favors and start giving it an
operating system.
**(Visual: Step-by-step prompt flow on screen: JD -> Interviewer Profiles ->
Simulation -> 3 Rounds -> Debrief.)**
Instructor: Now, let’s apply this framework to build your personal interview
simulator.
As we discussed in Module 6, asynchronous video interviews—like HireVue—are
becoming incredibly common. To beat them, we use the **HireVue Simulator
Prompt**. This prompt literally forces the Large Language Model to behave as an
automated interview platform.
Here is the exact 5-step prompt sequence to set this up:
1. Step 1: Feed the Job Description. You paste the target JD so the AI knows
the exact core skills and success metrics required.
2. Step 2: Feed the Interviewer Profiles. Paste the LinkedIn summaries of
three people you are likely to interview with.
3. Step 3: Activate the Simulation. Use our fill-in-the-blank prompt to tell
the AI to adopt the persona of the interviewer.
4. Step 4: Run 3 Rounds. We simulate a Screening round with HR, a Technical
round, and a Leadership round. 5. Step 5: Debrief and Score.
Imagine you're going for a Data Analyst role at a healthcare startup. You run
this prompt, and the AI asks you a tough question. You type or dictate your
STAR-method answer. The AI then instantly gives you a score out of 10 and shows
you exactly what a 9-out-of-10 answer would have sounded like.
If you run this simulation 10 times, by the time interview day arrives, you’ve
already had 10 practice runs with a bot that thinks exactly like your actual
human interviewer. It’s a completely unfair advantage.
**(Visual: Side-by-side resume vs. Job Description with hidden gaps
highlighted.)**
Instructor: Next, I want to introduce you to my absolute favorite tool in
the entire course: The Semantic Gap Analyzer.
Most people know to look for missing keywords when applying for a job. But the
Semantic Gap Analyzer goes much deeper. It compares your resume against the
target job description to find what I call "inferred skill gaps". These are the
discrepancies between the skills the employer implicitly requires and the ones
you are actually presenting.
When you run this prompt, it identifies four things:
1. Exact Keyword Mismatches. The obvious stuff.
2. Tonal Misalignments. Are you sounding like a middle-manager when they
want a scrappy startup operator?
3. Missing Soft Signals. Does your resume show cross-functional
collaboration, or just isolated technical work?
4. Implicit Seniority Markers. Are you using language that sounds too junior
for a VP role?
Here is the magic of the Semantic Gap Analyzer: It doesn't just tell you to "add
a keyword." It tells you to strategically reframe your experience. It might
say, "Don't just add the word 'strategy'; reframe your third bullet point to
signal strategic thinking by showing how you tied project outcomes to company
revenue.
This prompt bridges the gap between what you have done and what the Applicant
Tracking System—and the hiring manager—needs to see.
**(Visual: 7 cards appearing on screen with dark background and neon accent
colors.)**
Instructor: The Interview Simulator and the Semantic Gap Analyzer are just
the beginning. Below this video, you will find the complete GHFE AI Prompt
Library worksheet. It contains highly engineered prompts broken down into 7
specific categories:
1. Resume Optimization: The Achievement Miner. This prompt digs into your
vague job duties and translates them into CAR-method (Challenge-Action-Result)
bullet points loaded with hard metrics.
2. Cover Letter: The Problem-Solution-Proof Generator. This writes a
250-word cover letter that hooks the reader, articulates the company's "Bleeding
Neck" problem, and positions you as the exact solution.
3. LinkedIn: The Profile Optimizer. This prompt rewrites your headline and
summary to exploit LinkedIn's Interest Graph algorithm, maximizing your
discoverability to recruiters.
4. Company Research: The Intelligence Briefing. Feed this prompt a company
name, and it extracts their mission, recent challenges, and potential interview
talking points to build your 5-Layer Prep System.
5. Interview Prep: The Simulator. (Which we just covered).
6. Networking: The Outreach Composer. This prompt generates short, specific,
and flattering outreach messages and 3-touch follow-up sequences for your
informational interviews.
7. Negotiation: The Market Rate Triangulator. Feed it your competing offers
and market data, and it will script a collaborative, data-backed 4-step
negotiation conversation to ensure you get your Target or Stretch number.
These prompts do the heavy lifting for you. But, and this is a massive "but",
you cannot just copy and paste the outputs blindly. Which brings us to our
final, critical step.
**(Visual: Text on screen - "The AI Neutralization Effect" transitioning to "The
Copy-Paste Detection Test".)**
Instructor: Because AI is so accessible, the baseline quality of resumes and
cover letters has skyrocketed. Everyone sounds polished. But because everyone is
using the same AI models, every application is starting to look and sound
identical. I call this the AI Neutralization Effect.
If you let AI write 100% of your materials, the AI will "smooth out" all the
messy, non-linear, unique traits that make you a human being.
To beat this, we use the 10% Human Injection Framework. You let the AI
handle 90% of the structure, grammar, and keyword optimization. But you must
manually inject 10% of unmistakable humanity—a specific anecdote, a contrarian
insight, or a moment of strategic vulnerability.
Before you ever submit a resume or hit "send" on an AI-generated message, you
must run it through the Copy-Paste Detection Test.
Here is how it works: Take any paragraph from your resume or cover letter and
ask yourself: *"Could any other person with my job title have written this exact
sentence?"*
If the answer is yes, it fails the test. It is neutralized. You must rewrite it
by injecting a human detail—a specific metric, an inside joke from your team, or
an exact quote from a client. The future of career success isn't just about who
uses AI the best. It's about who remains the most human while using AI the
best. That 10% is your competitive moat.
(Visual: Instructor pointing to the camera with a confident smile.)
Instructor: Alright, here is your Coach's Challenge for this lesson. I want
you to download the AI Prompt Library worksheet right now.
Take your current resume and the job description for your top target role, and
run them through the Semantic Gap Analyzer. I want you to log at least 5 hidden
gap findings, apply the AI's suggested fixes, and then run it through the
Copy-Paste Detection Test to make sure it still sounds like you.
The tools in this library aren't magic—they are engineering. When you use them
correctly, you stop playing the volume game and start playing the precision
game.
Do the work, run the prompts, and I’ll see you in the next lesson where we'll
dive even deeper into overcoming the AI Neutralization Effect. Let's get to
work. (Visual: GHFE Logo animation & closing music.)