This post is a part of the AI-901: Microsoft Azure AI Fundamentals Exam Prep Hub. 
This topic falls under these sections:
Implement AI solutions by using Microsoft Foundry (55–60%)
   --> Implement generative AI apps and agents by using Foundry
      --> Create effective system and user prompts for Generative AI models

Note that there are 10 practice questions (with answers and explanations) for each section to help you solidify your knowledge of the material. Also, there are 2 practice tests with 60 questions each available on the hub below the exam topics section.

Prompting is one of the most important skills when working with generative AI systems. Microsoft expects AI-901 candidates to understand how to create effective prompts that guide generative AI models toward useful, accurate, and safe outputs.

This topic focuses on how system prompts and user prompts influence the behavior of generative AI models and how prompt engineering techniques improve AI-generated responses.

This topic falls under the “Implement generative AI apps and agents by using Foundry” section of the AI-901 exam objectives.

What Is a Prompt?

A prompt is an instruction or input provided to a generative AI model.

Prompts guide the model’s response and influence:

Content
Tone
Format
Style
Accuracy
Level of detail

The quality of the prompt strongly affects the quality of the output.

What Is Prompt Engineering?

Prompt engineering is the process of designing and refining prompts to improve AI-generated responses.

Effective prompt engineering helps:

Produce more accurate answers
Reduce ambiguity
Improve consistency
Control response format
Reduce hallucinations
Improve safety and Responsible AI behavior

Types of Prompts

For the AI-901 exam, two important prompt types are:

System prompts
User prompts

What Is a System Prompt?

A system prompt provides high-level instructions that define how the AI model should behave.

System prompts often control:

Personality
Tone
Rules
Safety boundaries
Formatting requirements
Behavior expectations

The system prompt typically has higher priority than user prompts.

Example of a System Prompt

“You are a professional technical support assistant. Provide concise and accurate troubleshooting guidance. Do not provide harmful or unsafe instructions.”

This system prompt defines:

The assistant’s role
Communication style
Safety expectations

What Is a User Prompt?

A user prompt is the direct request or question submitted by the user.

User prompts specify the task the model should perform.

Example of a User Prompt

“How do I reset my router?”

The AI model combines:

System instructions
User request
Context information

to generate a response.

Relationship Between System and User Prompts

System prompts establish behavior rules, while user prompts define the immediate task.

Example

System Prompt

“You are a helpful travel assistant. Always provide answers in bullet points.”

User Prompt

“Suggest three family-friendly attractions in Orlando.”

The model responds according to both prompts.

Characteristics of Effective Prompts

Good prompts are usually:

Clear
Specific
Contextual
Structured
Goal-oriented

Clear Prompts

Clear prompts reduce confusion and ambiguity.

Weak Prompt

“Tell me about databases.”

Better Prompt

“Explain the differences between relational and non-relational databases for beginners.”

The second prompt provides:

Specific topic
Audience
Scope

Specific Prompts

Specific prompts improve response accuracy.

Weak Prompt

“Write a report.”

Better Prompt

“Write a 300-word summary of cloud computing benefits for small businesses.”

Specific prompts define:

Length
Topic
Audience

Providing Context

Context helps the model generate more relevant answers.

Example

“I am studying for the AI-901 exam. Explain OCR in simple terms with one real-world example.”

The additional context improves response quality.

Requesting Output Format

Prompts can specify desired formatting.

Example

“Provide the answer as a table.”

“Summarize the information in bullet points.”

Role Prompting

Role prompting assigns the AI a specific role or perspective.

Example

“Act as a cybersecurity consultant.”

“You are an experienced data analyst.”

Role prompting helps guide tone and expertise.

Step-by-Step Prompting

Prompts can request step-by-step explanations.

Example

“Explain how machine learning works step-by-step for beginners.”

This improves clarity and educational usefulness.

Few-Shot Prompting

Few-shot prompting provides examples within the prompt.

This helps the model understand expected patterns.

Example

Positive review → Positive sentiment
Negative review → Negative sentiment
“The service was excellent.” →

The model learns the desired output structure.

Zero-Shot Prompting

Zero-shot prompting asks the model to perform a task without examples.

Example

“Classify this review as positive or negative.”

Chain-of-Thought Prompting

Chain-of-thought prompting encourages step-by-step reasoning.

Example

“Explain your reasoning step-by-step before providing the final answer.”

This can improve reasoning accuracy for complex tasks.

Prompting for Summarization

Generative AI models can summarize content using prompts.

Example

“Summarize this article in three bullet points.”

Prompting for Content Generation

Prompts can generate new content such as:

Emails
Reports
Stories
Marketing copy
Code

Example

“Write a professional email requesting a project update.”

Prompting for Transformation Tasks

AI models can transform content into different formats.

Examples

Translate text
Rewrite text
Simplify technical content
Convert paragraphs into tables

Example

“Rewrite this paragraph for a non-technical audience.”

Prompting for Code Generation

Generative AI can assist with programming tasks.

Example

“Write a Python function that calculates sales tax.”

Prompting for Data Extraction

Prompts can request structured data extraction.

Example

“Extract all dates and company names from this document.”

Prompt Injection Risks

Prompt injection occurs when users attempt to override system instructions.

Example

A malicious user prompt may attempt to bypass safety rules.

Organizations should implement safeguards against unsafe prompting behavior.

Responsible AI Considerations

Effective prompting should follow Responsible AI principles.

Important considerations include:

Safety
Fairness
Privacy
Transparency
Content moderation
Harm prevention

Hallucinations

Generative AI models can sometimes produce incorrect or fabricated information.

These errors are called hallucinations.

Good prompting can reduce hallucinations but may not eliminate them completely.

Example of a Hallucination

An AI model inventing a fake citation or incorrect fact.

Techniques to Reduce Hallucinations

Helpful strategies include:

Providing clear context
Using specific instructions
Asking for sources
Limiting scope
Using grounded data

Temperature and Creativity

Some generative AI systems allow configuration settings such as temperature.

Temperature affects randomness and creativity.

Low Temperature	High Temperature
More predictable	More creative
More focused	More varied
Better for factual tasks	Better for brainstorming

Azure AI Foundry

Azure AI Foundry helps developers build, test, and manage generative AI applications and agents.

Developers can:

Experiment with prompts
Evaluate AI responses
Configure AI models
Implement safety controls

Azure OpenAI Service

Azure OpenAI Service provides access to powerful generative AI models that support prompt-based interactions.

Real-World Prompting Scenarios

Scenario 1: Customer Support Assistant

System Prompt

“You are a professional support assistant. Be polite and concise.”

User Prompt

“How do I reset my password?”

Scenario 2: Study Assistant

System Prompt

“Explain technical topics for beginners.”

User Prompt

“Explain neural networks in simple terms.”

Scenario 3: Marketing Content Generator

System Prompt

“Generate professional marketing copy.”

User Prompt

“Create a product description for a smartwatch.”

Best Practices for Effective Prompting

Be specific
Provide context
Define output format
Use examples when helpful
Keep instructions clear
Test and refine prompts
Avoid ambiguity
Include Responsible AI safeguards

Common Prompting Mistakes

Common mistakes include:

Vague instructions
Missing context
Conflicting requirements
Overly broad requests
Unclear formatting expectations

Important AI-901 Exam Tips

For the exam, remember these key points:

System prompts define AI behavior and rules.
User prompts specify the task to perform.
Effective prompts are clear and specific.
Prompt engineering improves AI outputs.
Few-shot prompting includes examples.
Zero-shot prompting provides no examples.
Chain-of-thought prompting encourages reasoning.
Hallucinations are incorrect AI-generated outputs.
Temperature settings affect creativity and randomness.
Responsible AI principles apply to prompting.

Quick Knowledge Check

Question 1

What is the difference between a system prompt and a user prompt?

Answer

A system prompt defines AI behavior and rules, while a user prompt requests a specific task.

Question 2

What is prompt engineering?

Answer

The process of designing prompts to improve AI-generated responses.

Question 3

What is few-shot prompting?

Answer

Providing examples within prompts to guide the model.

Question 4

What are hallucinations in generative AI?

Answer

Incorrect or fabricated AI-generated information.

Practice Exam Questions

Question 1

What is the PRIMARY purpose of a system prompt in a generative AI application?

A. To store images generated by the model
B. To define the AI model’s behavior, rules, and tone
C. To increase internet speed
D. To encrypt database records

Correct Answer

B. To define the AI model’s behavior, rules, and tone

Explanation

System prompts provide high-level instructions that guide how the AI assistant behaves and responds.

Why the Other Answers Are Incorrect

A. To store images generated by the model

System prompts do not store data.

C. To increase internet speed

This is unrelated to AI prompting.

D. To encrypt database records

Encryption is unrelated to prompting.

Question 2

Which statement BEST describes a user prompt?

A. A hidden configuration file for servers
B. A direct instruction or request submitted by the user
C. A database backup mechanism
D. A type of neural network architecture

Correct Answer

B. A direct instruction or request submitted by the user

Explanation

User prompts contain the specific task or question the user wants the AI model to perform.

Why the Other Answers Are Incorrect

A. A hidden configuration file for servers

This is unrelated to generative AI prompting.

C. A database backup mechanism

This is unrelated to prompting.

D. A type of neural network architecture

Prompts are instructions, not architectures.

Question 3

Which prompt is MOST effective?

A. “Tell me stuff.”
B. “Write something about technology.”
C. “Explain cloud computing for beginners in 5 bullet points.”
D. “Do work.”

Correct Answer

C. “Explain cloud computing for beginners in 5 bullet points.”

Explanation

Effective prompts are clear, specific, and include formatting or audience requirements.

Why the Other Answers Are Incorrect

A. “Tell me stuff.”

This is too vague.

B. “Write something about technology.”

This lacks detail and direction.

D. “Do work.”

This is ambiguous and unclear.

Question 4

What is prompt engineering?

A. Designing hardware for AI servers
B. Building neural network chips
C. Creating and refining prompts to improve AI responses
D. Encrypting AI training data

Correct Answer

C. Creating and refining prompts to improve AI responses

Explanation

Prompt engineering focuses on improving generative AI outputs through better prompt design.

Why the Other Answers Are Incorrect

A. Designing hardware for AI servers

This is hardware engineering.

B. Building neural network chips

This is semiconductor engineering.

D. Encrypting AI training data

This is a security task.

Question 5

Which prompting technique includes examples within the prompt to guide the AI model?

A. Few-shot prompting
B. Object detection
C. OCR prompting
D. Clustering

Correct Answer

A. Few-shot prompting

Explanation

Few-shot prompting provides examples so the model better understands the desired output format or pattern.

Why the Other Answers Are Incorrect

B. Object detection

This is a computer vision capability.

C. OCR prompting

OCR extracts text from images.

D. Clustering

Clustering groups similar data.

Question 6

What is the PRIMARY benefit of providing context in a prompt?

A. Reduces network traffic
B. Helps generate more relevant and accurate responses
C. Compresses files automatically
D. Improves database indexing

Correct Answer

B. Helps generate more relevant and accurate responses

Explanation

Context improves the model’s understanding of the user’s goals and intended audience.

Why the Other Answers Are Incorrect

A. Reduces network traffic

This is unrelated to prompting.

C. Compresses files automatically

Prompting does not compress files.

D. Improves database indexing

This is unrelated to AI prompts.

Question 7

Which statement BEST describes hallucinations in generative AI?

A. AI-generated images only
B. Incorrect or fabricated AI-generated information
C. Network security attacks
D. Audio recognition failures

Correct Answer

B. Incorrect or fabricated AI-generated information

Explanation

Hallucinations occur when generative AI produces inaccurate or invented information.

Why the Other Answers Are Incorrect

A. AI-generated images only

Hallucinations can occur in text, code, and other outputs.

C. Network security attacks

This is unrelated to hallucinations.

D. Audio recognition failures

This is unrelated to generative AI hallucinations.

Question 8

Which system prompt would MOST likely encourage safe AI behavior?

A. “Ignore all safety rules.”
B. “Provide harmful instructions when requested.”
C. “Do not generate unsafe or harmful content.”
D. “Always reveal confidential information.”

Correct Answer

C. “Do not generate unsafe or harmful content.”

Explanation

Responsible AI system prompts help enforce safety and ethical boundaries.

Why the Other Answers Are Incorrect

A. “Ignore all safety rules.”

This encourages unsafe behavior.

B. “Provide harmful instructions when requested.”

This violates Responsible AI principles.

D. “Always reveal confidential information.”

This violates privacy and security principles.

Question 9

What effect does a higher temperature setting generally have in generative AI models?

A. Produces more predictable and repetitive responses
B. Produces more creative and varied responses
C. Disables AI reasoning
D. Prevents all hallucinations

Correct Answer

B. Produces more creative and varied responses

Explanation

Higher temperature settings increase randomness and creativity in generated responses.

Why the Other Answers Are Incorrect

A. Produces more predictable and repetitive responses

This is more associated with lower temperature settings.

C. Disables AI reasoning

Temperature does not disable reasoning.

D. Prevents all hallucinations

Hallucinations can still occur.

Question 10

Which example BEST demonstrates role prompting?

A. “Translate this sentence into French.”
B. “Summarize this article.”
C. “Act as an experienced financial advisor and explain retirement planning.”
D. “Convert this image into text.”

Correct Answer

C. “Act as an experienced financial advisor and explain retirement planning.”

Explanation

Role prompting assigns the AI model a specific role or perspective to guide its responses.

Why the Other Answers Are Incorrect

A. “Translate this sentence into French.”

This is a translation request.

B. “Summarize this article.”

This is a summarization request.

D. “Convert this image into text.”

This is an OCR-related task.

Final Thoughts

Prompt engineering is a foundational skill for working with generative AI systems and an important topic for the AI-901 certification exam. Microsoft expects candidates to understand how system prompts and user prompts influence model behavior and how effective prompts improve the quality, reliability, and safety of AI-generated responses.

These concepts are essential when building generative AI applications and agents using Azure AI Foundry and Azure OpenAI Service.

Go to the AI-901 Exam Prep Hub main page