AI-161 Using Amazon Q as Coding Assistant - Detailed Course Outline

Course Length

1 day or 2 half days

Course Overview

Amazon Q is one of the most popular alternatives to Github Copilot among coding assistant applications which help programmers to generate, refactor, explain and document program codes in dozens of languages as well to generate tests for the same codes.

Learning Objectives

At the end of the training participants will be able to:

• Understand the basic LLM and Amazon Q concepts
• Gain practical skills in prompt engineering
• Gain practical skills in using Amazon Q for code and test generation, code refactoring, explanation and documentation

Main Topics

• LLMs in a nutshell
• Introduction and main features of Amazon Q
• Test Driven Development with Amazon Q
• Amazon Q autocompletion
• Amazon Q Chat
• Prompt engineering for code generation
• Advanced features (Explain, Document, Refactor, Unit-test generation)
• Adding context

Structure

Roughly 50% lecture, 50% hands on lab exercises. The lab exercises are available either in a VS Code + Python, VSCode + C, C++ or VSCode + Java environment.

Target Audience

Software developers and testers as well as their technical managers who want to use Amazon Q in their daily work.

Prerequisites

Basic understanding of AI concepts, experience in using the programming language where Amazon Q would be used as a coding assistant, user experience with ChatGPT or similar chatbots.

Course Modules

Module 1: LLMs in a nutshell

Main parts and working of LLMs in a nutshell
Most important base LLM vendors and models
LLMs used in Amazon Q

Module 2: Introduction and main features of Amazon Q

Main features of Amazon Q
VS Code or IntelliJ basics
Core features of Amazon Q
Different Types of Interaction with Amazon Q
Using different LLMs (GPT, Claude Sonnet and Amazon Q's proprietary ones)
Test Driven Development with Amazon Q
Lab

Module 3: Amazon Q autocompletion

Auto completion
Accept suggestion, accept next word, next suggestion
With cycle menu
With shortcuts
Lab

Module 4: Amazon Q Chat

Amazon Q Chat: Inline commands
Amazon Q Chat: Chat panel
Amazon Q Chat: Tips & Tricks
Lab

Module 5: Prompt engineering for code generation

The 2 golden rules of prompt engineering
10 Prompting rules of thumb:
• Be concise and give clear instructions
• Be specific and include relevant details
• Add positive and negative prompts
• Define roles for the LLM
• Define roles for the LLM's audience
• Provide examples for the solution or response style (one-shot or few-shot prompting)
• Add relevant context
• Divide difficult tasks into subtasks (Prompt Chaining)
• "Let's think step by step" (Chain of Thought)
• Let the LLM ask questions
Lab

Module 6: Advanced features

Explain
Generate explanation for a code-block: With Code Lens, with Chat and linking the code block with @ in the Chat window as well as with code block selection and global menu
Documentation
Generate language specific documentation in the right format
Refactoring
Language specific refactoring options in the refactor menu
Unit-test generation
Lab

Module 7: Adding context

Context handling in Amazon Q
Custom chat instructions
Pinned contexts
Local indexes
Setting files and directories which won't be part of the context
Setting up team-wide repos for context
Using team-wide repos
Lab

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AI-161

Using Amazon Q as coding assistant and generator