AI Prompting: From Zero to FAANG-Ready
Stop guessing with AI. You'll go from typing half-formed questions into ChatGPT to writing prompts that consistently produce expert-level output — for coding, debugging, system design, and interview prep. Every lesson builds one entry in your personal Prompt Library.
Build it yourself, get guided when you are stuck, and leave with proof you can actually show.
What you learn by building this
- Understand why vague prompts fail and rewrite them on the spot
- Apply role, context, format, and constraint layers to any prompt
- Use chain-of-thought and few-shot techniques to tackle complex reasoning
- Build a battle-tested prompt library for coding workflows and debugging
- Use AI as a mock interviewer and system design coach for FAANG prep
- Design reusable prompt templates and know how to evaluate their reliability
Predict
What will happen?
Two Prompts. Same Goal. Very Different Results.
You've used ChatGPT before. So imagine you're stuck on a data structures problem at midnight before a technical screen. You type one of these two prompts.
Prompt A:
explain linked lists
Prompt B:
I'm a CS junior prepping for FAANG interviews. I understand arrays well. Explain linked lists to me focusing on: (1) what operations are O(1) vs O(n) and why, (2) when you'd choose a linked list over an array in an interview answer, and (3) one common mistake candidates make with linked lists in coding interviews. Keep it concise — I have 20 minutes to study.
Before you read on, write down your answers to these two questions:
- What does Prompt B tell the AI that Prompt A doesn't? List at least 4 specific things.
- Which prompt do you think gets a better response — and what would "better" even mean here?
Seriously, pause. Write it down. You'll understand the next section twice as well if you do.
What Prompt B Has That Prompt A Doesn't
Here's the full breakdown:
| What Prompt B includes | Why it matters |
|---|---|
| Who you are ("CS junior, FAANG prep") | The AI calibrates depth and tone |
| What you already know ("understand arrays") | No wasted explanation on things you know |
| Specific sub-questions (3 numbered items) | Forces a structured, focused answer |
| A time constraint ("20 minutes to study") | Keeps the response short and prioritized |
Prompt A gets a textbook definition. Prompt B gets a targeted study session.
The difference isn't luck. It's information. The AI isn't psychic — it responds to what you give it. Give it nothing, get nothing targeted back.
The Mental Model That Fixes Everything
Ask yourself this before you type any prompt:
"If I handed this task to a smart friend who knows nothing about my situation, what would they need to know to actually help me?"
That's it. That's the whole skill.
- Your background (so they calibrate correctly)
- What you're trying to accomplish (the actual goal, not just the surface question)
- What constraints you have (time, format, what NOT to do)
- What output you want (a list? an explanation? working code?)
Everything else in this course is practice making this faster and more precise.
Why This Is a Career Skill
FAANG engineers use AI tools — Copilot, Claude, ChatGPT — constantly. The ones who get the most out of them aren't necessarily the best coders. They're the ones who communicate with AI the same way they communicate with senior engineers: with precision, context, and a clear definition of done.
By the time you finish this course, you'll have a library of prompts for exactly the situations you'll face — debugging sessions, interview prep, system design, code reviews. Not generic prompts. Yours, tested and refined.
Challenge
Think first, then write
Your First Upgrade — And Your First Library Entry
Here are three prompts a real CS student might type. All three are weak. Your job: rewrite each one using what you just learned.
Open ChatGPT (or Claude) right now. Send your upgraded version. Compare the response to what the weak version would have gotten.
Prompt 1:
"explain binary search"
Think about what YOU need right now. Interview prep? Coding it from scratch? Understanding when to use it? Add your background, your goal, your constraints.
Prompt 2:
"help me with my resume"
A completely useless prompt as written. What role? What's already on it? What's weak? What's the company? Fill in the blanks.
Prompt 3:
"what is time complexity"
You're a CS student prepping for technical interviews. Push past the textbook definition. What level? What examples? What should it connect to?
After you send each upgraded prompt, check the response:
- Did it answer exactly what you needed, or did it have to guess?
- What follow-up question would you have needed to ask with the original weak version?
Stuck on Prompt 1? Try this structure: "I'm a [background]. I [what I already know]. Explain [topic] focusing on [specific thing I need]. I want [format / length]."
Save It
Once you've written and tested a prompt you're happy with, create your first library file.
In your project folder, create:
prompts/
fundamentals/
README.md
prompts/
fundamentals/
README.md
Inside README.md, paste in the best prompt you wrote today with a note on what made it work. This is entry #1 of your Prompt Library.
You should have a folder structure that looks like this when you're done:
prompts/
fundamentals/
README.md ← your first entry lives here
prompts/
fundamentals/
README.md ← your first entry lives here
How this build unfolds
Why Your Prompts Are Failing
Prompting for Code
Advanced Techniques
FAANG Interview Prep with AI
Learn by building your own version.
Remix this public project to open the workspace, follow the guided build, and let the AI mentor teach you through the work instead of doing it for you.