How Copilot Actually Works

Overview

When you type a question into Copilot and get an answer seconds later, a sophisticated process happens behind the scenes. Understanding how Copilot actually works — from checking your permissions to finding relevant data to generating responses — helps build trust in the technology and use it more effectively.

This video walks through the five-step process Copilot follows every time you submit a prompt: permission checking via Microsoft Graph, content discovery through Semantic Index, data retrieval and grounding, LLM processing, and response delivery. You’ll see that Copilot isn’t a black box — it’s a predictable, secure system designed for enterprise use.

What You’ll Learn

• The Five-Step Process: What happens from prompt submission to response delivery
• Microsoft Graph Role: How Copilot checks permissions and accesses organizational data
• Semantic Index: How Copilot quickly finds relevant content without reading every file
• Grounding and LLMs: How responses are generated based on your actual data, not guesses
• Security Boundaries: How your data stays protected throughout the process

Script

Hook

You type a question into Copilot. A few seconds later, you get an answer. But what just happened in those few seconds? Where did Copilot look? What did it read? How did it know what to say?

Let’s walk through the process step by step.

Step 1: You Submit a Prompt

Everything starts with your prompt. You could be in Word, Outlook, Teams, Excel, or the Copilot app. You type a question or give an instruction.

Your prompt stays within Microsoft’s security boundary. It’s not shared with other organizations or used to train foundation models. From the moment you hit enter, your request travels through a system designed for enterprise security and compliance.

Step 2: Microsoft Graph Context

Copilot connects to Microsoft Graph. Graph is Microsoft’s API that maps your organizational data — emails in Exchange, files in SharePoint and OneDrive, chats in Teams, your calendar, your contacts. It’s essentially a map of everything in your organization — who reports to whom, which files are related to which projects, who attended which meetings.

But here’s the critical part: Copilot checks your permissions first. You can only see data you already have access to. If you can’t open a document yourself, Copilot can’t read it for you. Existing access controls are respected completely.

Think of Microsoft Graph as the foundation that gives Copilot context about your work. It knows who you are, what projects you’re involved in, what data you have permission to access.

In GCC, GCC High, and DoD environments, Microsoft Graph data stays within your tenant’s security boundary. It never leaves your government cloud.

Step 3: Semantic Index

Now, Copilot doesn’t read every file you have access to — that would take too long. Instead, it uses something called Semantic Index.

Semantic Index pre-processes your M365 content. It creates a searchable understanding of topics, relationships, and concepts. It’s built on top of standard M365 search, but it goes deeper — understanding meaning, not just keywords. And it updates continuously as new content is created.

When you ask a question like “Summarize the last meeting about the budget,” Semantic Index helps Copilot find that specific meeting fast. Or “What did Sarah say about the deadline?” — Semantic Index finds Sarah’s messages related to deadlines.

This is what makes Copilot feel smart. It knows where to look. Semantic Index is why Copilot can answer questions about content from months ago in seconds, not minutes.

Step 4: Grounding and LLM Processing

Copilot retrieves the relevant content — emails, documents, meeting transcripts, whatever is related to your prompt. It combines your prompt with that grounded data and sends it to a large language model, or LLM.

Microsoft uses models from OpenAI — GPT-4 class models — running in Microsoft’s Azure cloud. Here’s what’s important: your organizational data is NOT used to train the LLM. The model processes your request in real time, but it doesn’t learn from your data.

The LLM generates a response based on three things: your specific prompt, the actual data Copilot retrieved, and enterprise safety guardrails built into the system.

This is grounding. Copilot isn’t guessing. It’s reading your actual content and summarizing it, drafting based on it, or answering from it. The response is tied to real data, not hallucinated from thin air.

Step 5: Response Delivery

Copilot returns the response to you in the same app where you asked. You see the generated content — a summary, a draft, an answer. But you also see citations showing which files or messages were used. You can click those citations to verify the source.

And you have options. You can regenerate the response if it’s not quite right. You can refine your prompt and try again. You can edit the output before using it.

The human is always in the loop. Copilot suggests. You decide.

The Full Loop

Let’s put it all together. When you submit a prompt, here’s what happens:

One: Copilot checks your permissions via Microsoft Graph.
Two: Semantic Index finds relevant content.
Three: Copilot retrieves that content and grounds the request.
Four: An LLM generates a response based on the grounded data.
Five: You receive the response with citations.

This happens in seconds, but it’s following a secure, predictable process. Your data stays in your tenant. Your permissions control access. The LLM only sees data you’re allowed to see.

Understanding this process helps you trust Copilot — and use it more effectively. You’re not typing into a black box. You’re interacting with a system that respects your organization’s security, checks your permissions, and grounds every response in real data.

Sources & References

• Microsoft 365 Copilot Overview — High-level architecture overview
• Semantic Index for Copilot — Semantic Index explanation and how it supports Copilot
• Microsoft Graph Overview — Microsoft Graph architecture and data access
• Microsoft 365 Copilot Privacy — Data processing, privacy, and tenant isolation