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21.6 GenAI Activities: Generative AI Inside Workflows

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UiPath Automation Developer Professional UiPath-ADPv1

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1 Chapter 1: Exam Overview & Blueprint Map

1.1 About the UiPath-ADPv1 Exam: Format, Fees & Policies 1.2 The Exam Topics Mapped to This Guide & a Study Plan

2 Chapter 2: REFramework Architecture & State Dynamics

2.1 State Machine Architecture & The Four Core States 2.2 Configuration Management: Settings, Constants, and Assets 2.3 Transaction Types & The Dispatcher vs. Performer Architecture 2.4 Adapting REFramework: Tabular Data, Collections & Linear Processes

3 Chapter 3: REFramework Exception Handling & Recovery

3.1 BusinessRuleException vs. System.Exception Handling Patterns 3.2 Retry Mechanisms: Queue Retries vs. REFramework MaxRetryNumber 3.3 Consecutive System Exceptions & Graceful Process Termination

4 Chapter 4: State Machines, Custom Log Fields & REFramework Testing

4.1 Building State Machine Projects in Studio 4.2 Logging Levels, Robot Logs & Custom Log Fields in REFramework 4.3 Testing a REFramework Project With and Without Orchestrator Queues

5 Chapter 5: LINQ Queries & DataTable Operations

5.1 LINQ Essentials for Collections & Filtering 5.2 Advanced DataTable Querying with LINQ 5.3 In-Memory DataTable Transformations & Column Operations

6 Chapter 6: Regex, DateTime, JSON & Invoke Activities

6.1 Regular Expressions & Text Parsing for Automation 6.2 DateTime Formatting, Timezone Conversions & TimeSpan Calculations 6.3 JSON Parsing, JSONPath & Serialization with Newtonsoft.Json 6.4 Invoke Method & Invoke Code Activities 6.5 Data Referencing in Invokes: DataTable, List & Dictionary Arguments

7 Chapter 7: Modern UI Automation & Unified Targeting

7.1 Modern Design Experience & The Unified Target Concept 7.2 Anchors, Relative Elements & UI Automation Activities 7.3 Dynamic Selectors, Variables & Wildcards in Selectors

8 Chapter 8: Object Repository, Computer Vision & Remote Runtime

8.1 Object Repository & UI Descriptor Libraries 8.2 AI Computer Vision for Unattended & Remote Interfaces 8.3 Automation in Virtual Environments & UiPath Remote Runtime

9 Chapter 9: Advanced UI Automation: Browser Modes, Agentic UI & macOS

9.1 Browser Automation with the WebDriver Protocol 9.2 Agentic UI Automation: ScreenPlay, Semantic Activities & Healing Agent 9.3 macOS Automation: Native Apps, Office Apps & AppleScript

10 Chapter 10: Advanced Control Flow & Exception Architecture

10.1 Flowcharts, Sequences & Parallel Control Flow Patterns 10.2 Global Exception Handler Architecture & ErrorAction Directives 10.3 TryCatch, Retry Scope & Custom Exception Architectures

11 Chapter 11: Orchestrator Queues & Transaction Management

11.1 Queue Architecture, SpecificContent & Transaction Lifecycles 11.2 Queue Items SLA, Postpone, Deadlines & Priority Scheduling 11.3 Bulk Operations, Transaction Status Updates & Queue Triggers

12 Chapter 12: Orchestrator Tenant & Folder Entities

12.1 Asset Types, Per-Robot Assets & External Credential Stores 12.2 Modern Folder Hierarchy, User/Robot Assignment & Permissions 12.3 Storage Buckets, Webhooks & Environment Promotion 12.4 Orchestrator Tags: Labels, Properties & Governance

13 Chapter 13: Unattended Execution, API Triggers & Cloud Robots

13.1 Unattended Job Execution: Folders, Priorities, Background vs. Foreground & Runtimes 13.2 API Triggers: Starting Jobs from External Systems over HTTP 13.3 Serverless and Cloud VM Robots: Cloud-Hosted Execution Options

14 Chapter 14: Monitoring, Recording & Solutions Management

14.1 Orchestrator Monitoring, Trigger Alerts & Job Health 14.2 Job and Transaction Recording: Troubleshooting Faulted Unattended Jobs 14.3 Solutions Management: Packaging and Deploying Automations with Their Resources

15 Chapter 15: Debugging, Remote Debugging & Profiling

15.1 Studio Debugging Tools: Breakpoints, Panels & Execution Control 15.2 Remote Debugging: Remote Machine and Unattended Robot Connections 15.3 Profile Execution: Finding Performance Bottlenecks in Studio

16 Chapter 16: RPA Testing & Test Suite

16.1 UiPath Test Suite Overview, Test Cases & Given-When-Then Structure 16.2 Data-Driven Testing, Test Verification Activities & Mock Activities 16.3 CI/CD Pipelines with uipcli & Test Manager Traceability

17 Chapter 17: Long-Running Workflows, Action Center & Apps

17.1 Long-Running Workflows: Persistence, Suspend and Resume 17.2 Action Center & Human-in-the-Loop Orchestration 17.3 UiPath Apps: Action Apps, Automations & Integration Service

18 Chapter 18: Trigger-Based Attended Automation & Callouts

18.1 Trigger-Based Attended Automation: Forms, Triggers & Global Variables 18.2 Running Local Triggers & the Event Inspection Tool 18.3 Callouts & Forms for Attended Guidance

19 Chapter 19: UiPath Assistant & Robot Sessions

19.1 UiPath Assistant: Automations, Reminders, Widgets & Configuration 19.2 Robot Session (Picture in Picture): Child Session vs. Virtual Desktop

20 Chapter 20: Autopilot for Developers & Autopilot for Everyone

20.1 Autopilot for Developers: AI Assistance in Studio and Studio Web 20.2 Autopilot for Everyone: Chat, Automations & Clipboard AI

21 Chapter 21: API Automation & Integration Service

21.1 Integration Service: Connectors, Connections & Connector Activities 21.2 API Workflows in Studio Web 21.3 HTTP Requests: Web APIs, the Orchestrator API & Connector Requests 21.4 Connector Builder: Custom Integration Service Connectors 21.5 Integration Service Event Triggers: Connected and Disconnected 21.6 GenAI Activities: Generative AI Inside Workflows

22 Chapter 22: Data Service, Context Grounding & Coded Workflows

22.1 Data Fabric (Data Service) Entities: Modeling Business Data 22.2 Data Fabric Activities and the Platform Connector 22.3 Context Grounding: Retrieval-Augmented Generation with Company Data 22.4 Coded Automations: Structure, Arguments & Invoking Workflows 22.5 Coded Automation Services: Orchestrator, UI Automation, Microsoft 365 & Custom Services

23 Chapter 23: Integrated Scenario Practice

23.1 Integrated Scenario Practice: Designing an End-to-End Solution

21.6 GenAI Activities: Generative AI Inside Workflows

Key Takeaways Explain This Topic AI

GenAI Activities cover content generation, summarizing, rewriting, translation, categorization, entity recognition, sentiment, PII filtering, and image tasks.

Context Grounding Search and Update Context Grounding Index connect workflows to grounded company knowledge.

Prefer the narrowest activity, constrain and validate outputs, and ground answers in company data.

Use PII Filtering and AI Trust Layer policies to protect data, and route consequential decisions to people.

Test GenAI steps with a fixed set of real inputs and re-run it when prompts or models change.

Last updated: September 2026

21.6 GenAI Activities: Generative AI Inside Workflows

Core Concept: GenAI Activities let a workflow use large language models for common tasks, such as summarizing, classifying, translating, extracting entities, or writing text, without building prompts and API calls from scratch. They run through UiPath's AI Trust Layer , where administrators govern models and data handling.

The Activities

Activity Use

Content Generation Send your own prompt, optionally grounded in a context grounding index, and get generated text

  • Summarize Text Summarize long text
  • Generate Email Draft an email from instructions and context
  • Rewrite / Reformat Rewrite text in another style, or reformat it
  • Translate / Detect Language Translate text; identify its language
  • Categorize Assign text to one of the categories you provide
  • Named Entity Recognition Find entities such as names, organizations, and dates
  • Sentiment Analysis Determine the sentiment of text
  • PII Filtering Detect and mask personal data
  • Image Analysis / Detect Object / Signature Similarity Describe images, find objects, compare signatures
  • Context Grounding Search Search a context grounding index and return relevant passages
  • Update Context Grounding Index Refresh an index after its source data changes
  • Get DeepRAG Analysis by ID Retrieve the result of a DeepRAG analysis over documents
  • Designing with GenAI Activities
  • Choose the narrowest activity

A dedicated activity such as Categorize or Summarize Text gives more predictable results than a general Content Generation prompt. Use Content Generation when no dedicated activity fits.

Constrain outputs

For Categorize , provide a closed list of categories and handle unexpected answers.

For Content Generation, ask for a specific format, such as JSON with named fields, and validate the result before using it.

Ground answers in company data

Answers about company policies or products should be grounded with a context grounding index , so the model uses retrieved passages rather than general knowledge.

Keep people in the loop

For decisions with consequences, such as refunds or legal replies, route the AI result to a person, for example with an Action Center task, before acting.

Protect data

Use PII Filtering before sending customer text to other systems or logs, and follow the tenant's AI Trust Layer policies on which data may be sent to models.

A Typical Pattern: Email Triage

Detect Language , then Translate non-English emails into English for the team.

Categorize into Complaint, Question, Order Change, or Other.

Sentiment Analysis flags strongly negative emails for priority.

Named Entity Recognition extracts the order number and customer name.

Context Grounding Search finds the relevant policy passage, and Generate Email drafts a reply using it.

The draft goes to an agent for review through an Action Center task.

Each step uses a focused activity, and the workflow validates each output before the next step.

GenAI Activities Versus Other AI Options

  • Option Best for
  • GenAI Activities Well-defined text or image tasks inside a workflow
  • Document Understanding Structured extraction from documents with validation
  • Autopilot for Everyone Business users chatting and running automations
  • Agents Multi-step, goal-driven work where an LLM decides which tools to call
  • Testing and Monitoring

Build a test set of real inputs with expected outputs, including hard cases, and re-run it when prompts or models change.

Log the category or decision and a short reason, not full sensitive texts.

Handle failures: wrap calls in Try Catch or Retry Scope, and decide what happens when the service is unavailable.

Watch for drift: if categories start to go wrong, review prompts and category descriptions.

Exam-Style Scenarios

Scenario 1: Support managers want a short summary of each long complaint in the ticket. Use Summarize Text, and store the summary in the ticket rather than the full text in logs.

Scenario 2: A process must answer employee questions using the HR handbook. Use Context Grounding Search on an index of the handbook, then Content Generation grounded on the retrieved passages, and send uncertain answers to an HR specialist.

Scenario 3: Customer emails arrive in five languages, but the team reads only English. Use Detect Language and Translate before categorizing, and keep the original text for the reply.

Scenario 4: A legal team needs names, dates, and company names from contracts listed for review. Use Named Entity Recognition, and route the list to a reviewer before it is used in any decision.

Key Terms

Context grounding index: a searchable store of company documents used to ground model answers.

DeepRAG: an analysis over documents whose result is retrieved with Get DeepRAG Analysis by ID.

AI Trust Layer: the governance layer through which UiPath AI features reach models, with policies for data and model use.

Common Traps

Using Content Generation for tasks a dedicated activity handles better.

Trusting generated output without validation, especially numbers and IDs.

Sending personal data without PII Filtering where policy requires it.

Forgetting to refresh an index with Update Context Grounding Index after the source documents change.