Lesson 1: Mastering Prompt Engineering: From Basics to Breakthrough Techniques

Introduction to Prompt Engineering

Prompting isn’t about tricking AI — it’s about teaching it to think clearly. Large language models (LLMs) like ChatGPT, Claude, and Gemini are fundamentally prediction engines. Given an input (your prompt), they generate the most likely next word based on patterns learned from massive amounts of text. That makes your prompt the blueprint.

A vague request will produce vague output. But a clear, well-structured prompt? That’s how you get useful, specific, and often surprisingly intelligent responses.

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Watch the Lesson Overview

Watch the video above to see examples of model behavior in action — including how temperature, output length, and prompt structure affect the results.

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Why Prompt Engineering Matters

Prompt engineering is the skill of designing effective instructions for AI. It doesn’t require coding, ML knowledge, or advanced technical skills. What it does require is the ability to be clear, specific, and intentional with your inputs.

A good prompt tells the model:

• Who it is (“You are a product strategist…”)
• What to do (“Summarize this in 3 bullet points.”)
• What it’s working with (Input: text, table, scenario, etc.)
• How to respond (e.g., bullet list, JSON, tone, word count)

Once you learn how to structure that input — and iterate when things go sideways — you can:

• Summarize complex documents in seconds
• Brainstorm new product ideas or messaging
• Analyze data, extract patterns, or generate insights
• Role-play expert conversations
• Automate repetitive writing and analysis tasks

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Key Definitions & Terminology

✍️ You’ll see these terms throughout the course. We’ll introduce each one here, and go deeper in later lessons.

• Prompt Engineering: The practice of designing structured inputs that guide LLMs to produce useful and accurate outputs.
• Token: A chunk of text (roughly ~¾ of a word) used by LLMs to process and predict sequences.
• Output Length: The maximum number of tokens a model can generate.
• Temperature: Controls randomness in output. High = creative, Low = consistent.
• Top K / Top P: Sampling controls that limit token selection based on rank (Top K) or cumulative probability (Top P).
• Zero-Shot Prompting: Giving a task without examples.
• Few-Shot Prompting: Supplying 2–5 examples to guide output format or behavior.
• System / Role / Contextual Prompting: Techniques for setting model behavior, tone, and context.
• Chain of Thought (CoT): Encourages the model to reason step-by-step before producing an answer.
• Self-Consistency: Running the same prompt multiple times and selecting the majority answer.
• Tree of Thoughts: A method for branching reasoning paths for complex tasks.
• ReAct: Combines reasoning and action via tools (e.g., search, code execution).
• Automatic Prompt Engineering: Using LLMs to generate better prompts programmatically.

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Prompt Examples

Zero-Shot

"Classify the sentiment of this movie review: 'Her is a disturbing study revealing the direction humanity is headed.' Use: Positive, Neutral, or Negative."

‍Few-Shot with Structured Output

"Parse this pizza order into JSON format. Example: 'I want a small pizza with cheese, tomato sauce, and pepperoni.' → { size: 'small', ingredients: [...] }"

Chain of Thought

"When I was 3 years old, my partner was 3x my age. Now I’m 20. How old is my partner? Let’s think step by step."

System + Role Prompt

System Instruction: "You are a travel guide."
Prompt: "I’m in Amsterdam. Recommend three museums."

ReAct with Tools

"Using search, find how many children each Metallica band member has. Sum the total."

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Use When / Avoid When Guidance

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Pro Tips

• Start with simple, clear instructions — add complexity only if needed.
• Always specify the desired output format (e.g., JSON, list, paragraph).
• Use temperature = 0 for reproducible outputs; > 0.7 for ideation.
• Combine techniques (e.g., few-shot + chain of thought) to improve accuracy.
• Leverage LLMs to write better prompts for themselves (automatic prompt engineering).

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Step-by-Step Framework

How to Write a High-Quality Prompt:

1. Define your goal clearly.
2. Choose the right prompting technique (e.g., zero-shot, role-based).
3. Specify the desired format and structure.
4. Set temperature/output length appropriately.
5. Test and iterate — update based on results.

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“Try This Prompt” Challenges

Challenge 1
Pause here. Write a zero-shot prompt for an LLM to classify customer support tickets.

Challenge 2
Now, write a few-shot version of that same task with 3 examples. Compare results.

Challenge 3
Pick a task you frequently do at work. Write a chain-of-thought version of the prompt.

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Real-World Examples

🧴 Marketing
"Act as a brand strategist. Write 3 email subject lines for a new skincare product launch."

📩 Sales
"You're a B2B account executive. Draft a cold outreach email to a VP of Product at a SaaS company."

🔬 Research
"Summarize the key findings from this abstract in 3 bullet points."

🛠 Operations
"Convert this unstructured job description into a standardized competency matrix."

🎓 Education
"Write 3 quiz questions that test understanding of step-by-step prompting."

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Recap

You now understand how LLMs respond to structured inputs, and how to guide their behavior with temperature, examples, and format cues. These fundamentals apply to every prompting technique you’ll learn going forward.

Next up: How to Tune Model Settings for Better Output — a deeper dive into the variables that shape how your prompt performs.

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