04 LLMs & Copilot Architecture — Theory & Concepts

About this video

• Video Title: 04 LLMs & Copilot Architecture — Theory & Concepts
• Channel: Skills Pragati
• Speakers: (Speaker names not available in transcript)
• Duration: 00:25:13

Overview

This video explains the fundamental concepts of Generative AI and Large Language Models (LLMs), detailing how they create new content. It breaks down the architecture of Microsoft 365 Copilot, illustrating how it integrates LLMs with an organization's data through components like the Copilot Orchestrator, Microsoft Graph, and Semantic Index. The video also covers crucial aspects like grounding, context awareness, data privacy, and security principles related to Copilot's operation.

Key takeaways

• Generative AI vs. Traditional AI: Generative AI creates new content (text, images, code, etc.), while traditional AI focuses on retrieving, classifying, or predicting from existing data.
• LLMs as Engines: Large Language Models (LLMs) are neural networks trained on vast text data, forming the core of generative AI tools like Microsoft 365 Copilot. They work by predicting the most probable next "token" (subword unit) based on learned patterns.
• Microsoft 365 Copilot Architecture: This is a sophisticated orchestration system combining LLMs with organizational data. It involves the Copilot Orchestrator, Microsoft Graph (for data access), Semantic Index (for relevant content retrieval), and Azure OpenAI Service (for LLM processing).
• Grounding and RAG: Grounding connects Copilot's responses to specific organizational data using Retrieval-Augmented Generation (RAG), ensuring relevance and accuracy, often indicated by citation references.
• Context Awareness: Copilot's responses are shaped by three types of context: the specific Microsoft 365 app being used, the user's work content (files, emails, etc.), and web data (when enabled).
• Data Security and Privacy: Copilot operates within tenant boundaries, never shares data with other tenants, does not use organizational data for training LLMs, and strictly enforces existing Microsoft 365 permissions.

1. What is the key innovation within the transformer architecture that allows LLMs to understand long-range dependencies in language?
2. What is a "token" in the context of LLMs?
3. What is the difference between a "prompt" and a "completion" when interacting with Copilot?
4. How does Microsoft 365 Copilot access an organization's data?
5. What are the three types of context that shape Copilot's responses?

Here's a multiple-choice quiz based on the video content:

1. What is the primary function of Generative AI, as described in the video? a) Retrieving information from existing data b) Classifying data into predefined categories c) Creating new, original content d) Predicting future outcomes based on historical data

2. Which of the following is NOT an example of content that Generative AI can create? a) Text b) Images c) User permissions d) Music

3. What is the core technology that powers generative AI tools like Microsoft 365 Copilot? a) Traditional AI algorithms b) Large Language Models (LLMs) c) Relational Databases d) Machine Learning Classifiers

4. LLMs learn statistical patterns by being trained on: a) A small, curated dataset of factual information b) Billions of text documents from the internet, books, and code repositories c) Only real-time user prompts d) Spreadsheet data and financial reports

5. When interacting with Copilot, what is the term for your natural language instruction? a) Completion b) Token c) System Prompt d) Prompt