This post is a part of the AI-103: Develop AI Apps and Agents on Azure Exam Prep Hub. 
This topic falls under these sections:
Implement text analysis solutions (10–15%)
   --> Apply language model text analysis
      --> Implement solutions to extract entities, topics, summaries, and structured JSON outputs by using generative prompting and Foundry Tools

Note that there are 10 practice questions (with answers and explanations) at the end of each section to help you solidify your knowledge of the material. Also, there are 2 practice tests with 60 questions each available from the hub's main page below the exam topics section.

Introduction

Modern AI applications increasingly rely on language models to transform unstructured text into structured, actionable information. Organizations use generative AI systems to:

Extract entities
Detect topics
Generate summaries
Produce structured JSON outputs
Automate workflows
Enrich search and analytics systems

For the AI-103 certification exam, you should understand how to implement text analysis workflows using:

Generative prompting
Multimodal and language models
Structured outputs
Azure AI Foundry tools
Prompt orchestration
Responsible AI practices

This topic falls under:

“Apply language model text analysis”

What Is Text Analysis?

Definition

Text analysis is the process of extracting meaningful information from unstructured text.

Examples include:

Entity extraction
Topic classification
Sentiment analysis
Summarization
Categorization
Structured data generation

Why Generative AI Improves Text Analysis

Traditional NLP systems often relied on:

Rule-based processing
Fixed schemas
Pretrained classifiers

Generative AI systems provide:

Flexible extraction
Contextual understanding
Natural language reasoning
Dynamic schema generation
Few-shot adaptability

Common Text Analysis Tasks

Entity Extraction

Identifying important entities within text.

Examples:

Names
Organizations
Dates
Locations
Products
Financial values

Example Entity Extraction

Input:

Contoso signed a contract with Fabrikam on March 5, 2026.

Extracted entities:

			
{
  "organizations": [
    "Contoso",
    "Fabrikam"
  ],
  "date": "March 5, 2026"
}

		

Topic Extraction

What Is Topic Extraction?

Topic extraction identifies the primary themes discussed within text.

Example Topics

Document:

			
The company discussed quarterly cloud migration costs and AI infrastructure scaling.

Detected topics:

Cloud computing
AI infrastructure
Financial operations

Summarization

What Is Summarization?

Summarization condenses large amounts of text into shorter, meaningful summaries.

Types of Summaries

Extractive Summarization

Selects important text directly from the source.

Abstractive Summarization

Generates new language-based summaries.

Generative AI commonly uses abstractive summarization.

Example Summary Prompt

Summarize this customer support conversation in three sentences.

Structured JSON Outputs

Why Structured Outputs Matter

Structured outputs improve:

Automation
API integration
Data pipelines
Analytics
Workflow orchestration

Example Structured Output

			
{
  "customer_sentiment": "negative",
  "issue_type": "billing",
  "priority": "high"
}

		

Prompt Engineering for Text Analysis

Why Prompt Engineering Matters

Prompts strongly influence:

Extraction quality
Consistency
Formatting
Hallucination frequency

Example Entity Prompt

Extract all people, organizations, and dates from the following text.

Example JSON Prompt

Return the output strictly as valid JSON.

Example Topic Classification Prompt

Identify the top three business topics discussed in this document.

Few-Shot Prompting

What Is Few-Shot Prompting?

Few-shot prompting provides examples within prompts.

Example

			
Input: "Invoice overdue for 45 days"
Output:
{
  "category": "accounts receivable"
}

		

Few-shot prompting improves consistency and accuracy.

Chain-of-Thought Reasoning

Some workflows encourage reasoning before output generation.

Example:

Analyze the text step-by-step before generating the final JSON output.

Structured Output Validation

Generated JSON should be validated to ensure:

Proper formatting
Required fields
Valid schema structure

Example Validation Concerns

Potential issues:

Missing fields
Invalid JSON syntax
Hallucinated values
Unexpected schema changes

Hallucinations in Text Analysis

What Are Hallucinations?

Hallucinations occur when models:

Invent entities
Create unsupported summaries
Generate incorrect classifications

Example Hallucination

Input:

Meeting scheduled for Tuesday.

Incorrect output:

			
{
  "location": "New York"
}

The location was never mentioned.

Reducing Hallucinations

Strategies include:

Grounded prompts
Retrieval augmentation
Schema validation
Confidence scoring
Human review
Explicit formatting instructions

Retrieval-Augmented Generation (RAG)

What Is RAG?

RAG combines:

Retrieval systems
Vector search
Generative models

to improve grounding and reduce hallucinations.

Example RAG Workflow

User submits question
Relevant documents retrieved
LLM analyzes retrieved content
Structured output generated

Azure AI Foundry

Microsoft provides:
Azure AI Foundry

to help build and orchestrate AI workflows.

Foundry Capabilities

Azure AI Foundry supports:

Prompt flows
Model orchestration
Evaluations
Safety testing
Workflow automation
AI experimentation

Prompt Flows

What Are Prompt Flows?

Prompt flows visually orchestrate:

Inputs
LLM calls
Validation steps
Tool integrations
Output processing

Example Prompt Flow

Receive document
Extract entities
Classify topics
Generate summary
Return JSON response

Multi-Step Text Analysis Pipelines

Organizations commonly chain multiple operations:

OCR
Summarization
Classification
Translation
Entity extraction

Example Enterprise Workflow

Upload support ticket
Detect language
Extract entities
Summarize issue
Generate structured JSON
Route to support queue

Azure OpenAI Service

supports:

Generative prompting
Structured outputs
Summarization
Topic extraction
Entity extraction

Azure AI Language

supports:

Named entity recognition
Classification
Summarization
Sentiment analysis

Azure AI Search

supports:

Vector search
Hybrid search
Retrieval workflows
RAG architectures

Azure Functions

commonly orchestrates:

Text pipelines
Event triggers
Automated workflows

Security and Responsible AI

Text analysis systems must handle:

Sensitive data
PII
Confidential information
Harmful prompts

Responsible AI Considerations

Organizations should:

Validate outputs
Monitor hallucinations
Protect privacy
Audit workflows
Apply content filtering

Privacy Considerations

Text may contain:

Personal information
Financial data
Medical information
Corporate secrets

Organizations should:

Encrypt data
Restrict access
Mask sensitive fields

Human-in-the-Loop Review

Human review may be necessary for:

Legal workflows
Healthcare systems
Financial reporting
High-risk classifications

Observability and Monitoring

Production systems should monitor:

Latency
Token usage
Hallucination frequency
JSON validation failures
Prompt injection attempts
Cost
Throughput

Cost Optimization

Generative AI pipelines can become expensive.

Optimization strategies include:

Shorter prompts
Chunking large documents
Smaller models where appropriate
Caching results
Batch processing

Example Structured Extraction Workflow

A legal firm may:

Upload contracts
Extract entities
Detect clauses
Generate summaries
Produce structured JSON metadata
Store searchable outputs

This demonstrates:

Entity extraction
Summarization
Structured outputs
Workflow orchestration

Best Practices for Text Analysis Workflows

Use Explicit Prompt Instructions

Improve consistency and formatting.

Validate JSON Outputs

Prevent downstream parsing failures.

Ground Responses in Source Data

Reduce hallucinations.

Use Multi-Step Pipelines

Separate extraction, classification, and summarization stages.

Monitor Hallucinations

Track unsupported outputs.

Protect Sensitive Data

Apply privacy and security controls.

Support Human Review

Especially for high-risk workflows.

Exam Tips for AI-103

For the AI-103 exam, remember these important concepts:

Entity extraction identifies structured information within text.
Topic extraction identifies major themes.
Summarization condenses large text into concise outputs.
Structured JSON outputs improve automation and integrations.
Prompt engineering strongly affects extraction quality.
Few-shot prompting improves consistency.
Hallucinations generate unsupported or incorrect outputs.
RAG improves grounding using retrieved documents.
Azure AI Foundry supports prompt flows and orchestration.
Azure OpenAI Service supports generative text analysis workflows.
JSON validation is important for reliable downstream processing.

Practice Exam Questions

Question 1

What is the purpose of entity extraction?

A. Compressing text files
B. Identifying structured information such as names and dates
C. Encrypting JSON outputs
D. Scaling databases dynamically

Answer

B. Identifying structured information such as names and dates

Explanation

Entity extraction identifies meaningful structured information within text.

Question 2

What is topic extraction?

A. Compressing prompts
B. Removing hallucinations automatically
C. Encrypting documents
D. Identifying major themes discussed within text

Answer

D. Identifying major themes discussed within text

Explanation

Topic extraction identifies the primary subjects or themes in content.

Question 3

Why are structured JSON outputs useful?

A. They simplify automation and system integration
B. They eliminate OCR workflows
C. They reduce internet bandwidth usage
D. They disable hallucinations

Answer

A. They simplify automation and system integration

Explanation

Structured outputs are easier for applications and APIs to process programmatically.

Question 4

What is a hallucination in generative AI?

A. A valid JSON schema
B. Unsupported or invented model output
C. A GPU optimization technique
D. An OCR extraction method

Answer

B. Unsupported or invented model output

Explanation

Hallucinations occur when models generate incorrect or fabricated information.

Question 5

What is few-shot prompting?

A. Disabling prompts entirely
B. Compressing token usage automatically
C. Providing examples within prompts to guide model behavior
D. Encrypting prompt flows

Answer

C. Providing examples within prompts to guide model behavior

Explanation

Few-shot prompting improves output quality by demonstrating desired behavior.

Question 6

Which Azure service supports prompt flow orchestration?

A. Azure AI Foundry
B. Azure DNS
C. Azure Firewall
D. Azure CDN

Answer

A. Azure AI Foundry

Explanation

Azure AI Foundry supports prompt flows, orchestration, and AI workflow management.

Question 7

What is Retrieval-Augmented Generation (RAG)?

A. Combining retrieval systems with generative AI for grounded responses
B. Compressing OCR results
C. Encrypting vector embeddings
D. Removing JSON outputs

Answer

A. Combining retrieval systems with generative AI for grounded responses

Explanation

RAG retrieves relevant information before generating responses.

Question 8

Why should generated JSON outputs be validated?

A. To disable summarization
B. To reduce OCR latency
C. To ensure schema correctness and prevent parsing failures
D. To eliminate vector search

Answer

C. To ensure schema correctness and prevent parsing failures

Explanation

Validation ensures outputs are properly structured and usable downstream.

Question 9

Which Azure service supports generative summarization and entity extraction?

A. Azure Virtual WAN
B. Azure ExpressRoute
C. Azure Firewall
D. Azure OpenAI Service

Answer

D. Azure OpenAI Service

Explanation

Azure OpenAI Service supports generative AI-based text analysis workflows.

Question 10

What is a best practice for reducing hallucinations?

A. Disable monitoring systems
B. Automatically trust all outputs
C. Use grounded prompts and validation workflows
D. Avoid structured outputs

Answer

C. Use grounded prompts and validation workflows

Explanation

Grounding and validation help reduce unsupported or fabricated outputs.

Go to the AI-103 Exam Prep Hub main page