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AI Career Skills Course
Module 2: Prompt Engineering
AI, Machine Learning and Generative AI Basics
LLMs, Tokens and Context Windows
Hallucinations, Confidence and AI Limitations
Clear Task Prompts and Output Formats
Context, Examples and Few-Shot Prompting
Prompt Debugging and Iteration
AI for Writing, Summaries and Emails
AI for Research and Meeting Notes
AI Automation for Routine Office Work
AI Study Planning and Concept Learning
AI Practice Questions and Feedback Loops
Using AI Responsibly in Assignments
AI for Code Explanation and Debugging
AI for Tests, Refactors and Code Review
AI Pair Programming Workflow
AI for Spreadsheet Cleanup and Analysis
Asking Better Data Questions
Chart, Insight and Decision Summaries
Embeddings and Semantic Search
RAG Workflows and Knowledge Bases
AI Agents, Tools and Automation
Privacy, Sensitive Data and Access Control
Bias, Fairness and Harmful Output Checks
Evaluating AI Answers Before Use
CONTENTS

Context, Examples and Few-Shot Prompting

Use background material and examples to guide model behavior more reliably.

AI Career Skills Course
Module 2: Prompt Engineering
AI career skills
generative AI
+7
May 28, 2026
29
A

Learning Outcome

Use background material and examples to guide model behavior more reliably.

Core Ideas

  • Context: Background needed to answer well.
  • Few-shot example: A sample input-output pair that guides behavior.
  • Style guide: Rules for tone and formatting.
  • Negative example: A sample showing what to avoid.

Career Use Case

A teacher creating feedback comments can provide two good examples so AI follows the expected tone and structure.

Practical Workflow

  1. Start by naming the outcome: what should improve after using Context, Examples and Few-Shot Prompting?
  2. Add the input material, constraints, and success criteria before asking for output.
  3. Ask for assumptions and uncertainty when the answer affects a real decision.
  4. Verify important claims, numbers, and policy statements before publishing or acting.

Hands-On Mini Task

  • Create one input-output example that shows the model exactly how a final answer should look.
  • A good example reduces ambiguity and prevents the model from inventing an unsuitable style.
  • Before moving on, explain how Context and Few-shot example change the decision.

Common Mistakes

  • Using a generic prompt when the task needs clear context.
  • Accepting polished wording as proof of accuracy.
  • Sharing private data without redaction or approval.
  • Skipping a final human review for important decisions.

Quick Revision

Module 2: Prompt Engineering lesson 5 is about practical judgement: use AI to increase speed, but keep the goal, context, evidence, and accountability clear.

FAQs

Is Context, Examples and Few-Shot Prompting only for technical users?

No. The course treats AI as a practical workplace and learning skill, with technical depth only where it improves judgement.

Should I trust AI output immediately?

No. Use AI to accelerate work, then verify facts, privacy, source fit, and reasoning before relying on the result.

What should I practice after this lesson?

Create one input-output example that shows the model exactly how a final answer should look.

How does the linked practice quiz help?

The practice quiz checks the lesson concepts immediately with feedback, while the paid mock bundle uses separate assessment questions.

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Practice Quiz 5: Context, Examples and Few-Shot Prompting
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Lesson 2 of 3 in Module 2: Prompt Engineering
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Clear Task Prompts and Output Formats
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Prompt Debugging and Iteration
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