Lesson 1: Introduction to Prompt Engineering

Learn with Tex – Prompt Engineering Course

Table of Contents

Welcome to Learn with Tex

If you’ve ever felt frustrated that an AI didn’t give you the answer you expected, you’re not alone. Almost everyone has typed something into a chatbot, received a flat or confusing response, and thought, “Well, this doesn’t work as well as people claim.”

The truth is: AI isn’t broken — the way we ask questions often is.

This course, Learn with Tex, is here to fix that. Over the next six lessons, you’ll learn how to craft powerful prompts that transform AI from a clumsy tool into a sharp assistant. Whether you’re a student, a writer, a business professional, or simply someone curious about the future of technology, prompt engineering is a skill you can’t afford to ignore.

Today’s lesson — our starting point — will give you a strong foundation. We’ll explore what prompt engineering is, why it matters, and how you can begin applying it right now.

What Exactly Is Prompt Engineering?

At its core, prompt engineering is about communication.

Think about how you’d ask a friend for help. If you say, “Write something about history,” you might get a vague answer. But if you say, “Hey, can you write me a short 300-word piece about how the printing press changed Europe, in a simple style that high school students can understand?” — the response will be far more useful.

AI works the same way.

A prompt is simply the input you give to an AI. But a crafted prompt — one that includes role, context, detail, and intent — can unlock far better results. Prompt engineering is the skill of designing those crafted prompts with purpose.

It’s not about tricking AI. It’s about collaborating with it effectively.

A Short History of Prompting

To understand why prompt engineering feels revolutionary, it helps to step back and see how we got here.

1950s–1970s: Computers only understood rigid code. Users had to memorize exact commands.
1980s–2000s: The personal computer era made interaction easier with menus and graphical interfaces. But still, machines dictated how we worked with them.
2018–2020: Large language models like GPT-2 and GPT-3 showed a new possibility: machines could understand natural language and respond conversationally. Researchers noticed something fascinating — the phrasing of a question could drastically change the answer.
2021 onward: The rise of consumer AI apps (ChatGPT, Groq, Claude, Gemini) turned prompt writing into an everyday activity. Entire job roles called “prompt engineers” appeared, and companies began teaching employees how to communicate with AI systems.

For the first time in history, plain language has become a kind of programming.

You don’t need to be a coder to get value from AI. You just need to learn how to talk to it well.

Why Prompt Engineering Matters

Some people dismiss prompt engineering as a gimmick. They say, “The AI should just know what I mean.”

But here’s the reality:

AI is not a mind reader. It predicts words based on probability.
A vague input → vague, generic, sometimes wrong output.
A clear, structured input → targeted, insightful, and creative output.

This difference matters everywhere:

A business professional might save hours by asking AI to draft a polished proposal.
A teacher could generate lesson plans tailored to a specific age group.
A developer could speed up debugging by asking AI to focus only on error handling.

Prompt engineering is quickly becoming a core digital literacy skill. In the same way email and search engines changed work in the 1990s, prompts are shaping how we work in the 2020s.

The bottom line: Better prompts equal better results.

Examples of Bad vs Good Prompts

Let’s make this concrete.

Example 1: Writing

❌ Bad Prompt: “Write about dogs.”

✅ Good Prompt: “Act as a pet expert. Write a 200-word blog post explaining why dogs make great companions for families with kids. Use a friendly tone.”

Example 2: Education

❌ Bad Prompt: “Explain gravity.”

✅ Good Prompt: “Explain gravity to a 6th-grade student in simple words. Use a fun analogy, like a trampoline, and keep it under 150 words.”

Example 3: Coding

❌ Bad Prompt: “Write Python code.”

✅ Good Prompt: “Write a Python function that takes a list of names and returns them sorted alphabetically. Include comments and a usage example.”

Notice the difference: the good prompts provide context, role, and format. They’re easier for AI to follow — and easier for you to evaluate.

The Anatomy of a Good Prompt

One simple framework can help you craft better prompts instantly:

[Role] + [Task] + [Context] + [Format] + [Tone/Audience]

Let’s break it down:

Role: Who should the AI act as? (teacher, expert, storyteller, marketer)
Task: What exactly do you want it to do? (write, explain, summarize, code)
Context: What’s the background? (audience, purpose, scenario)
Format: How should it be delivered? (essay, list, script, table)
Tone/Audience: What style or voice? (casual, professional, playful, simple)

Example using the formula:

“Act as a fitness coach. Create a 7-day beginner workout plan for someone who wants to exercise at home. Present it in a table format with exercises, duration, and rest time. Use an encouraging and supportive tone.”

That’s far more powerful than just: “Write a workout plan.”

Real-World Use Cases

Here are a few ways different industries already use prompt engineering:

Writers: generate outlines, rewrite drafts, brainstorm character arcs.
Educators: create lesson plans, generate quizzes, explain complex ideas in simple terms.
Businesses: draft contracts, summarize meetings, brainstorm product names.
Developers: optimize code, explain errors, generate test cases.
Designers: create brand copy, generate ad variations, prompt image models.
Researchers: summarize studies, identify trends, check consistency in data.

Prompt engineering is universal. If your field uses information, prompts can supercharge it.

Practice Exercises

Let’s make this interactive.

Rewrite a vague prompt you’ve used before into a structured one.
Try this: Start with “Write a story about a detective.” Then improve it: “Write a 500-word short story about a detective in a futuristic city who uses AI to solve crimes. Keep the tone suspenseful, like a noir thriller.”
Compare the two outputs. Which feels more professional?

🚀 Try It Yourself (Powered by Groq)

Here’s your first real exercise.

Sample Prompt:

“Explain prompt engineering to a 12-year-old using a superhero example.”

Now try writing your own! (Limit: 3 prompts per user IP per day)

Why Prompt Engineering Works (The Science Behind It)

When we give vague instructions, both humans and machines struggle. Our brains naturally crave structure — and AI models, which work by predicting language patterns, thrive when they’re given context and direction.

In short: clarity reduces randomness.

The more context you give → the more relevant the answer.
The more structure you provide → the more useful the format.
The more role and tone you define → the more natural the response feels.

This is why prompt engineering feels less like “cheating” and more like “effective communication.”

Lesson Summary

Prompt engineering = the skill of writing effective instructions for AI.
Vague prompts → vague answers. Clear prompts → useful answers.
Use the Role–Task–Context–Format–Tone framework.
Every industry can benefit from prompt engineering.
Practicing prompts daily will make you faster and sharper with AI tools.

What’s Next?

In Lesson 2, we’ll go deeper into the anatomy of a good prompt. You’ll learn reusable templates, prompt patterns, and tricks professionals use to get consistently high-quality results.

👉 Go to Lesson 2 →

⚡ This was Lesson 1 of the Learn with Tex: Prompt Engineering Course. Keep practicing, and remember: every great AI response begins with a great prompt.