From the course: AI-Powered Development: GitHub Copilot in Visual Studio Code
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AI, LLMs, and Copilot explained
From the course: AI-Powered Development: GitHub Copilot in Visual Studio Code
AI, LLMs, and Copilot explained
It's my belief that there's a lot of confusion around the term AI, especially with all the buzz and hype we're seeing in the tech world right now. Let's clarify what AI actually means before seeing it in action. In this section, we'll focus on machine learning and large language models, two concepts that power most of the buzz. By grounding ourselves here, we'll cut through the hype and build a solid framework to understand the rest of this course. Much of today's excitement, and funding, and press coverage centers around one branch of AI, large language models, which drive generative workflows. Understanding LLMs helps us see why chatbots, code assistants, and creative tools have exploded onto the scene. LLMs are a type of machine learning model. The key feature of any machine learning model is that it discovers patterns from data rather than being explicitly programmed. These models can be trained on all sorts of data, images, audio, tabular data, and text. Training data comes from…
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Contents
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Completions, chat, and agent modes8m 3s
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Code competion: JavaScript2m 59s
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Making sense of nondeterministic predictions1m 38s
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Code completion: Python1m 46s
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HTML and CSS completion1m 40s
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More inline suggestions features3m 6s
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Use comments to guide completion2m 32s
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Use chat to ask code specific questions2m 48s
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Use chat to explore a project4m 18s
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Explore the Word Frequency Analyzer page1m 30s
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Explain a section of code5m 53s
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Guidance on potential issues2m 26s
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Get refactoring suggestions2m 57s
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Have Copilot draft an editing prompt3m 8s
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Review the hidden reasoning workflow2m 34s
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Inline chat: Exploring the code editor2m 34s
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Create new chats3m
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Chat panel vs. agent sessions3m 3s
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Manage chat threads and history1m 57s
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Rename a chat thread55s
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Save a chat thread2m 54s
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Delete a chat thread1m 56s
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Customize chat settings2m 15s
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Use voice and audio1m 47s
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Prompt engineering for the agent4m 11s
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Start an agent-assisted refactor2m 55s
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Add blocked words to the UI2m 17s
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Add additional files2m 34s
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Clean up invalid charactors57s
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Modernize the UI layer3m 29s
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Refactor review: Outcomes and impact1m 53s
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Prototype a Greenfield project7m 26s
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Tour the graphic generation site1m 53s
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Import a saved chat conversation3m 22s
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How to prompt a graphic generation site4m 20s
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Examine the generated code and UI5m 2s
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Tour the Pedal and Pines site8m 13s
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Review refactored site sections4m 36s
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Review updated code and UI4m 52s
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Prototype setbacks and frustrations1m 44s
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Postmortems and prompt refinement2m 6s
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Understand cloud and background agents5m 12s
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Plan mode in Copilot: Quick review2m 25s
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Session panel2m 23s
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Create a plan5m 15s
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Save plan as markdown1m 42s
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Run the plan on background agent1m 40s
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Use terminal with background agent2m
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The Git worktree2m
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Review the changes1m
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Merge the changes1m 28s
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MCP overview6m 35s
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Query a database with an MCP server4m 9s
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Generate TypeScript DB access code1m 23s
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Understand mcp.json setting2m 13s
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Enable catalog browsing in the Extensions panel1m
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Install other MCP servers with the Extension panel3m 12s
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Find MCP servers on GitHub2m 53s
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