The "Learn X Before Y" Prompt

How to Turn Foundational Advice Into Viral Content That Actually Helps People

In every technical field, there's a constant pressure to learn the next big thing. The hype around new tools and advanced frameworks is so loud that it's easy for people to forget a simple truth: you can't build a skyscraper on a foundation of sand.

Most content chases the hype, adding to the noise. But the most trusted senior voices do the exact opposite. They constantly remind their audience of the fundamentals. They provide clarity, not complexity.

They use a simple but powerful framework to do this: The "First, Then" approach.

It works by creating a clear, sequential ladder of knowledge, showing the foundational skill you must master first, before moving on to the advanced tool then.

This format is a powerful way to signal wisdom and build trust. It's an act of mentorship that helps people navigate their careers more effectively.

Let's deconstruct a perfect example of this in action, and then I'll give you The "First, Then" Prompt so you can provide your own foundational guidance.

The Psychology Behind "First, Then"

🙅 The Authority of Restraint

When everyone else is shouting about the latest framework, the person who says "wait, learn this first" instantly stands out. It's counterintuitive content that signals experience.

Think about it: Only someone who's been burned by skipping steps would know to warn others. Only someone who's mastered both the fundamentals AND the advanced tools could map the proper sequence.

This restraint creates immediate credibility. You're not selling the dream: you're preventing the nightmare.

🎁 The Gift of Permission

Here's what really happens when someone reads a "First, Then" post: relief.

Finally, someone is giving them permission to slow down. Permission to master the basics without FOMO. Permission to build depth before breadth.

In a world of "10x engineers" and "learn AI in 30 days," this permission is oxygen. It says: "You're not behind. You're building properly."

🧙‍♂️ The Mentorship Signal

The "First, Then" format does something powerful: it positions you as a mentor, not just an expert.

An expert's focus is on the subject: they tell you how a tool works. A mentor's focus is on the person: they tell you how a career works.

The expert's value is in their knowledge. A mentor's value is in their wisdom, which is earned through the scars of experience. The "First, Then" list is a map of those scars; it's a direct transfer of hard-won career strategy.

This is the difference between providing answers and providing guidance. You're not just showing them the destination; you're giving them the map and warning them about the washed-out bridges and dead ends you discovered yourself.

Anatomy of the "First, Then" Post

Let's break down why each component works:

🪝 The Hook: Humble Confidence

"A gentle reminder for Data Scientists (you'll thank me later)"

What it does:

• "Gentle reminder" = non-threatening, caring tone

• Specific audience = immediate relevance

• "(you'll thank me later)" = confidence without arrogance

The parenthetical promise is genius. It's not "you NEED this" (pushy) or "this might help" (weak). It's "you'll thank me later"—the exact thing a wise mentor would say.

✔︎ The Core List: Sequential Wisdom

Each line follows the same structure:

"Learn [Foundation] before [Advanced Tool]"

The three elements that make it work:

1. Verb consistency - Every line starts with "Learn"

2. Clear sequencing - "before" creates explicit order

3. Relevant pairings - Each connection makes logical sense

The repetition creates rhythm. The specificity creates value. Together, they create memorability.

⚗️ The Closing Thesis: Crystallized Wisdom

"Remember: Basics First"

Two words that summarize the entire philosophy. It's not a lecture: it's a principle. Not a rule: a reminder.

The colon creates pause. The brevity creates impact. It's the kind of line people screenshot and share.

The Secret Sauce: Pairing Logic

Here's where most people screw up the "First, Then" format: They pair random fundamentals with random tools.

Too Generic:

• Learn math before AI

• Learn basics before advanced

• Learn theory before practice

Too Specific:

• Learn gradient descent before Adam optimizer

• Learn SQL joins before window functions

• Learn Python 3.9.1 before Python 3.9.2

Just Right:

• Learn Statistics before Deep Learning

• Learn Pandas before PySpark

• Learn Linear Regression before Transformers

The sweet spot: One conceptual level apart. Close enough to see the connection, far enough to show progression.

Building Your Own "First, Then" Posts

Here's my process for creating these mentorship moments:

Step 1️⃣: Identify the Hype Traps

Set a timer for 10 minutes. List every "hot tool" in your field that beginners chase:

• What's trending on LinkedIn/Substack?

• What's in every job description?

• What do juniors ask about most?

Step 2️⃣: Map the Foundations

For each hyped tool, ask:

• What would I need to know to truly understand this?

• What did I learn first that made this click?

• What do people skip that causes problems later?

Step 3️⃣: Test the Sequence

Write each pairing and check:

• Is the connection obvious once stated?

• Would skipping the first genuinely hurt?

• Does the pairing tell a mini-story?

Cut any pair where the connection feels forced.

Step 4️⃣: Craft the Rhythm

Structure for impact:

• Start with the most relatable pairing

• Build complexity gradually

• End with the most aspirational pairing

• Aim for 8-12 pairs (cognitive sweet spot)

Advanced Variations

Once you master the basic format, try these variations:

📜 The "Timeline" Version

"A roadmap for Data Scientists (learned the hard way)"
Learn Python in Month 1-3 before Libraries in Month 4-6
Learn SQL in Month 4-6 before Spark in Month 7-9
[Continue with timeline pairs]
Remember: Foundations Take Time

❌ The "Mistake" Version

"What I wish I knew as a junior Data Scientist"
Don't learn PySpark before mastering Pandas
Don't learn Deep Learning before Statistics
[Continue with "don't" pairs]
Remember: Mistakes Cost More Than Time

👔 The "Interview" Version

"What FAANG actually tests (not what you think)"
They test Statistics, not Deep Learning frameworks
They test Pandas, not PySpark optimization
[Continue with interview reality pairs]
Remember: Fundamentals Get Jobs

🆚 The "Comparison" Version

"Old School vs New School (both matter)"
Statistics (1900s) enables Deep Learning (2010s)
SQL (1970s) enables Modern Data Stacks (2020s)
[Continue with timeline comparisons]
Remember: New Tools, Timeless Principles

Common Mistakes to Avoid

🪑 Being Condescending

"First, Then" should feel helpful, not superior. You're a guide, not a gatekeeper.

🔗 Creating False Dependencies

Not every pairing needs strict sequencing. Be honest about what's truly foundational.

🖼️ Ignoring Context

A bootcamp grad needs different advice than a PhD. Acknowledge your audience's starting point.

🌀 Overcomplicating the Format

The power is in simplicity. Resist adding explanations to each line.

🤖 Forgetting the Human Element

Add warmth. The "gentle reminder" and "you'll thank me later" touches matter.

Here’s the prompt: