Module 10 Lesson 1: Prompt Engineering Mastery

If the LLM is a powerful engine, the Prompt is the steering wheel. Even the best model will fail if given a vague or confusing prompt.

In this lesson, we move from "Chatting" with AI to "Engineeering" responses. We'll learn the three most important types of prompting used by professionals today.

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1. Zero-Shot vs. Few-Shot

This refers to how many examples you give the model.

• Zero-Shot: You give the model an instruction with NO examples.
  • Prompt: "Classify this email sentiment: I love this product!"
• Few-Shot: You provide 2-3 examples of the task before asking the question.
  • Prompt: "Positive: I love this! | Negative: This is bad. | Neutral: It is okay. | Classify: I love this product!"

The Rule: Use Zero-Shot for simple, common tasks. Use Few-Shot for complex formatting, niche corporate styles, or logic tasks.

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2. Chain-of-Thought (CoT)

This is the "Secret Weapon" of prompt engineering. By asking the model to "Think step-by-step," you force it to allocate more compute to the reasoning phase before it gives the final answer.

Why it works: Remember our lesson on the Transformer (Module 5)? By writing out its reasoning, the model's future tokens can "Attend" back to its previous logic steps, making it much more likely to get a difficult math or logic problem right.

graph TD
    Prompt["Hard Logic Question"] --> Simple["Direct Prompting: 'The answer is X' (Often Wrong)"]
    Prompt --> CoT["Chain of Thought: 'Let's think step by step... Step 1... Step 2... therefore X'"]
    CoT --> Success["High Accuracy Result"]

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3. The Perfect Prompt Structure (CREATE)

When writing a complex prompt, follow this 5-point checklist:

1. Character: Give the AI a role (e.g., "You are a senior Python developer").
2. Request: Tell it exactly what to do (e.g., "Refactor this code").
3. Examples: Provide 1-2 examples of what a good output looks like.
4. Audience: Tell it who the response is for (e.g., "For a junior intern").
5. Type: Specify the format (e.g., "Output as a Markdown table").

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4. Why "Stop Sequences" Matter

In professional apps, you don't always want a full conversation. You might want the AI to only output one word. You can use Stop Sequences to tell the model: "Once you hit a newline or a period, stop immediately." This saves money and makes your app feel much snappier.

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Lesson Exercise

Goal: Compare CoT results.

1. Ask an LLM: "How many 'r's are in the word Strawberry?" (Common failure point for basic models).
2. If it gets it wrong, ask it again but add: "Think step-by-step. List every letter one by one, and then count only the letter 'r'."
3. Does the answer change?

Observation: You've just seen how forcing the model to "show its work" changes its statistical trajectory towards the truth!

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Summary

In this lesson, we established:

• Few-shot prompting is the fastest way to teach a model a specific format.
• Chain-of-Thought (CoT) significantly improves reasoning by creating a "logical trail" for the model to follow.
• Structured prompts (Character, Request, Examples) lead to more reliable results.

Next Lesson: We go beyond text. We'll learn about Connecting to Tools, and how models can use "Function Calling" to interact with the real world.