English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 60 Lessons (4h 55m) | 1.38 GB
Deep dive into Artificial Intelligence to improve developer productivity
AI holds tremendous potential and moves ahead at breakneck speed. One of the areas in which it shines is helping you increase your productivity as a developer, in and out of Visual Studio. In this deep dive course, which starts where the related getting started course stopped, you’ll learn how to do that. The course is focused on GitHub Copilot, but other assistants and approaches are also covered. You’ll learn how to use it in a variety of IDEs. You’ll learn how it can help write tests, optimise code, commit code, review PRs, and even iteratively help you implement a user story with Copilot Edits. After that, a substantial part of the course is dedicated to evaluating LLMs: you’ll get a taste of Mistral Large, Claude Sonnet, GPT4o and others. A variety of LLMs is then used to teach you advanced prompt engineering techniques to improve the results you’ll get. The final section of the course is about extension and AI agents: you’ll learn how to write your own agent that integrates with GitHub Copilot.
Table of Contents
1 Welcome
2 What will you learn in this course?
3 Who is the course for and prerequisites
4 Introduction
5 Positioning GitHub Copilot
6 Setting Up GitHub Copilot in VS Code
7 Working With GitHub Copilot in VS Code
8 Setting Up GitHub Copilot in JetBrains Rider
9 Working With GitHub Copilot in JetBrains Rider
10 Working With GitHub Copilot in the GitHub CLI
11 Working With GitHub Copilot in Other IDEs
12 Section Recap
13 Introduction
14 Getting to Know the Demo Codebase
15 Unit Tests, Integration Tests and End-to-End Tests
16 Generating Dummy Data
17 Generating and Running Unit Tests
18 Generating and Running Integration Tests
19 WebApplicationFactory: Integration Tests or End-to-End Tests?
20 Generating and Running End-to-End Tests
21 Generating Test Requests
22 Section Recap
23 Introduction
24 Improving Your Codebase
25 Working With Copilot Edits
26 Section Recap
27 Introduction
28 Committing Code and Creating Pull Requests in Visual Studio
29 Creating Pull Requests on GitHub
30 Section Recap
31 Introduction
32 LLMs, From Generic to Specific
33 How LLMs Are Rated
34 Should You Be Running Benchmarks Yourself?
35 Comparing Popular LLMs
36 Evaluating ChatGPT (GPT4o) for Development
37 Evaluating Mistral (Mistral Large) for Development
38 Evaluating Claude (Sonnet) for Development
39 Integrating Claude in Your IDE
40 Evaluating Amazon Q Developer for Development
41 Integrating With LLMs From Code
42 Section Recap
43 Introduction
44 Prompts, Prompt Engineering and the Lingo Problem
45 Manipulating LLM System Prompts
46 Zero-, One-, and Few-shot Prompting
47 Weighted Prompting
48 Chain-of-Thought (CoT) Prompting
49 Reasoning and Acting (ReAct) Prompting
50 Retrieval-augmented Generation (RAG) Prompting
51 Section Recap
52 Introduction
53 About AI Agents and Extensions
54 Discovering Extensions
55 Installing and Using an Extension
56 Creating an AI Agent with Retrieval-augmented Generation (RAG) Prompting Support
57 Creating a Custom AI Agent: Plumbing
58 Creating a Custom AI Agent: Implementation
59 Creating a Custom AI Agent: Tightening the Implementation
60 Section Recap
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