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Mastering Code Optimization and Customization with AI

Packt via Coursera

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Overview

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Generative AI is transforming software development by enabling intelligent assistants that help developers write, optimize, and troubleshoot code faster. This course focuses on Amazon Q Developer, an AI-powered assistant designed to accelerate coding workflows and improve developer productivity. You will learn how to integrate Amazon Q Developer into your development environment and use it to generate, explain, and improve code across multiple programming languages. The course also covers practical workflows such as auto-code generation, debugging, feature development, and security scanning while working with AWS services. What makes this course unique is its practical focus on real-world AI-assisted development. You will apply generative AI concepts directly to coding tasks, using Amazon Q Developer to automate repetitive work and streamline the development lifecycle. This course is designed for software developers, application builders, and cloud professionals who want to enhance productivity using AI-powered coding assistants. Basic programming knowledge and familiarity with AWS services are recommended. This course is part two of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.

Syllabus

Boost Coding Efficiency for Command-Line and Shell Script with Auto-Code Generation

In this section, we explore auto-code generation for CLI and shell scripts using Amazon Q Developer, focusing on system administration tasks, script optimization, and explainability for improved productivity.

Boost Coding Efficiency for JSON, YAML, and HCL with Auto-Code Generation

In this section, we explore auto-code generation for JSON, YAML, and HCL using Amazon Q Developer, focusing on IaC efficiency, code optimization, and practical implementation techniques.

Customizing Code Recommendations

In this section, we explore implementing Amazon Q customizations for code support, evaluating performance, and configuring user access and IDE integration to align with internal libraries and coding standards.

Understanding Code References

In this section, we explore code references, their management, and open source compliance. Key concepts include enabling, disabling, and reviewing code suggestions for responsible integration.