1.2 Context & Grounding: Work Files, Web Data and App Context

Microsoft AI Business Professional AB-730

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1 Introduction & Exam Overview

0.1 About the AB-730 Exam 0.2 How to Use This Study Guide

2 Chapter 1: Understand Generative AI Capabilities Across Microsoft 365

1.1 How Copilot Works: LLMs, Microsoft Graph & the Security Boundary 1.2 Context & Grounding: Work Files, Web Data and App Context 1.3 Chat Experiences vs. Agent Experiences 1.4 When to Create Your Own Agent 1.5 Copilot Capabilities Across Microsoft 365 Apps

3 Chapter 2: Responsible AI & Data Protection Practices

2.1 Generative AI Risks: Fabrications, Prompt Injection & Over-Reliance 2.2 Verification Steps: Citation Checks & Human Review 2.3 Recognizing & Mitigating Risks to Sensitive Data 2.4 How Data Protection Restricts Prompt Results

4 Chapter 3: Creating and Managing Prompts

3.1 Creating an Effective Prompt: The GCSE Framework 3.2 Selecting Resources to Reference in a Prompt 3.3 Saving and Sharing Prompts in Copilot Prompt Gallery 3.4 Scheduling Prompts

5 Chapter 4: Managing Conversations and Notebooks

4.1 Finding Previous Conversations 4.2 Renaming and Deleting Chats 4.3 Adding a Conversation to a Copilot Notebook

6 Chapter 5: Creating and Managing Microsoft 365 Copilot Agents

5.1 Agent Store vs. Creating a New Agent 5.2 Creating an Agent from a Template 5.3 Configuring an Agent That Has Knowledge 5.4 Configuring Instructions, Capabilities & Suggested Prompts 5.5 Sharing an Agent with Team Members

7 Chapter 6: Drafting and Analyzing Business Content

6.1 Creating a New Document from a Prompt 6.2 Generating a Document from an Existing Document 6.3 Generating a Management Summary 6.4 Moving Data & Insights Between Microsoft 365 Apps 6.5 Drafting Communications with Copilot in Outlook

8 Chapter 7: Meetings, Collaboration and Personalization

7.1 Using Copilot for Meetings: Setup & Live Intelligence 7.2 Meeting Recap, Transcripts & Catch-Up 7.3 Copilot Pages for Collaboration 7.4 How Copilot Uses Memory & Instructions

1.2 Context & Grounding: Work Files, Web Data and App Context

Key Takeaways Explain This Topic AI

Grounding is the end-to-end Retrieval-Augmented Generation (RAG) process that enriches prompts with relevant enterprise context.

The five-stage grounding pipeline covers prompt capture, pre-processing/Graph search, LLM enrichment, post-processing/Purview compliance, and response generation.

The Semantic Index for Copilot uses vector embeddings to map conceptual meaning and entity relationships across Microsoft 365 assets.

Copilot can draw context from active open documents, stored OneDrive/SharePoint files, Exchange emails, Teams transcripts, and optional Bing web grounding.

When web grounding is enabled, search queries dispatched to Bing are stripped of user identity and tenant identifiers to protect corporate privacy.

Last updated: August 2026

1.2 Context & Grounding: Work Files, Web Data and App Context

Quick Answer: Grounding is the process of supplying relevant, real-time context to a Large Language Model so that its output is factual, accurate, and tailored to your organization. Microsoft 365 Copilot uses Retrieval-Augmented Generation (RAG) across a 5-stage lifecycle: (1) Prompt Capture, (2) Pre-Processing via Microsoft Graph, (3) Prompt Enrichment to the LLM, (4) Post-Processing for Purview compliance and citations, and (5) Response Delivery. Context is gathered from active app documents, internal Graph files, and optional de-identified Bing web searches.

Without grounding, a Large Language Model is limited to its pre-trained general knowledge. While it can write code or explain historical events, it knows nothing about your company's sales pipeline, client deliverables, or team project timelines. Grounding is the foundational mechanism that transforms a generic foundational model into a customized business assistant.

1. The 5-Stage Copilot Grounding Lifecycle (RAG Pipeline)

Microsoft 365 Copilot implements an enterprise pattern known as Retrieval-Augmented Generation (RAG) . Instead of modifying the foundation model's weights, the system retrieves relevant documents and metadata at runtime and injects them directly into the prompt payload sent to the LLM. ┌─────────────────────────────────────────────────────────────────────────────┐

│ THE 5-STAGE GROUNDING LIFECYCLE │
│ │
│ [1. PROMPT CAPTURE] │
│ User inputs prompt + Active app context (e.g., open Word document) │
│ │ │
│ ▼ │
│ [2. PRE-PROCESSING & RETRIEVAL] │
│ Query parsed ──► Graph & Semantic Index search ──► User ACLs evaluated │
│ (Optional: De-identified Bing web search executed) │
│ │ │
│ ▼ │
│ [3. PROMPT ENRICHMENT & LLM INVOCATION] │
│ Enriched Payload = System Rules + Retrieved Chunks + User Prompt │
│ Sent securely via TLS to Azure OpenAI Service │
│ │ │
│ ▼ │
│ [4. POST-PROCESSING & COMPLIANCE] │
│ Responsible AI filters ──► Purview DLP validation ──► Citations verified │
│ │ │
│ ▼ │
│ [5. RESPONSE DELIVERY] │
│ Structured response rendered in UI with interactive footnotes & citations │
└─────────────────────────────────────────────────────────────────────────────┘
Detailed Breakdown of the 5 Stages
Stage 1: Prompt Submission & Context Capture

The user enters a natural language prompt in a Microsoft 365 app (e.g., "Summarize the open feedback items from yesterday's steering committee meeting" ). The client app captures the immediate environment: active open files, cursor location, highlighted text, and conversation history.

Stage 2: Pre-Processing and Enterprise Retrieval

Before calling the LLM, Copilot preprocesses the prompt:

Analyzes user intent and extracts key entities (e.g., "steering committee meeting", "yesterday").

Queries Microsoft Graph and the Semantic Index to find relevant emails, meeting transcripts, and SharePoint files.

Applies the user's Microsoft Entra ID access controls so that only authorized content is retrieved.

If web search is enabled, issues an anonymized query to Bing for public context.

Stage 3: Prompt Enrichment & LLM Invocation

Copilot constructs an enriched prompt (also known as a metaprompt). This structured payload includes:

System instructions establishing role, behavior, tone, and safety constraints.

The retrieved enterprise document snippets (the "grounding data").

The user's original query.

This combined payload is transmitted securely to the LLM in Azure OpenAI Service.

Stage 4: Post-Processing, Purview, and Responsible AI Checks

When the LLM generates a draft completion, it returns to the Copilot orchestration layer for rigorous post-processing:

Responsible AI Filters: Scans for harmful content, hate speech, self-harm, sexual content, or jailbreak attempts.

Microsoft Purview Integration: Validates Data Loss Prevention (DLP) rules and ensures Sensitivity Labels (e.g., Confidential / Internal Only ) are respected and propagated.

Citation Generation: Cross-references the generated text against the retrieved source snippets to attach verifiable citations and URLs.

Stage 5: Final Response Delivery

The grounded, verified response is rendered in the user's interface, complete with numbered citations, source file cards, and actionable follow-up buttons.

2. Context Sources for Grounding

Copilot pulls context dynamically from three primary tiers of information: ┌─────────────────────────────────────────────────────────────────────────────┐

│ COPILOT CONTEXT TIERS │
│ │
│ ┌──────────────────────┐ ┌──────────────────────┐ ┌────────────────────┐ │
│ │ Active App Context │ │ Internal Graph Data │ │ Public Web Grounding│ │
│ │ │ │ │ │ │ │
│ │ • Open Word document │ │ • OneDrive files │ │ • Real-time Bing │ │
│ │ • Selected table │ │ • SharePoint sites │ │ search results │ │
│ │ • Active email thread│ │ • Exchange mailboxes │ │ • De-identified │ │
│ │ • Live Teams meeting │ │ • Teams chat history │ │ enterprise query │ │
│ └──────────────────────┘ └──────────────────────┘ └────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────────┘
1. Active App Context (Local Workspace)

When invoking Copilot inside an app, the immediate canvas provides implicit grounding:

Word / PowerPoint: The text, structure, slide layouts, and comments of the currently open file.

Excel: The active worksheet, data tables, headers, and cell ranges.

Outlook: The selected email message or the full preceding thread history.

Teams: The live meeting transcript or the active channel thread.

2. Internal Enterprise Work Data (Microsoft Graph)

When using Copilot Chat (Business Chat) or referencing external files using the / or @ symbol, Copilot searches across all organizational repositories the user has permission to view. This includes .docx , .pptx , .xlsx , .pdf , OneNote notebooks, emails, and calendar events.

3. Public Web Grounding (Bing Integration)

When a prompt requires public market data, industry standards, or recent global events, Copilot can ground its response in the live web via Bing.

Exam Warning: How does Microsoft protect enterprise privacy during web grounding? When web grounding is triggered, Copilot extracts search terms from the user's prompt and removes all organizational identifiers, tenant metadata, user names, and file contents before dispatching the query to the Bing search service. The query is routed through a dedicated enterprise pipeline; it is never linked to the user's personal identity, nor is it stored to build user advertising profiles.

Tenant administrators can globally manage or disable web grounding within the Microsoft 365 admin center under Copilot settings.

3. Semantic Index for Copilot: Vector Search vs. Keyword Search

Traditional search engines rely on lexical keyword matching (e.g., BM25 algorithms). If you search for "Q3 medical device revenue roadmap" , a keyword index searches exclusively for files containing those exact words.

The Semantic Index for Copilot uses advanced machine learning models to generate high-dimensional vector embeddings that capture conceptual meaning, intent, and relationships.

Feature Traditional Keyword Search Semantic Index for Copilot (Vector Search)

Matching Principle Exact string and lexical keyword matching Mathematical similarity between high-dimensional vector embeddings

Synonym Recognition Requires explicit synonym dictionaries or boolean operators Natively understands that "revenue", "sales", "top-line growth", and "turnover" are related concepts

Contextual Awareness Treats words in isolation Understands how words interact in sentences and paragraphs

Cross-Modal Mapping Limited to plain text matching Maps concepts across emails, chats, presentations, and transcripts

User Relevance Static document rank Personalizes results based on user's working relationships and recent Graph activity

User-Level vs. Tenant-Level Semantic Indexing

User-Level Index: Indexes the user's personal work items (personal OneDrive documents, personal mailbox, 1:1 chat history, personal calendar).

Tenant-Level Index: Indexes shared SharePoint Online sites and public channels that are accessible to broad groups or the entire tenant, strictly honoring file-level permissions.