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Course

AI-Assisted Coding for Developers

Intermediate1 hr 30 min

Boost your coding with AI—guide your coding assistant to write, test, and document code effectively.

Python1 hr 30 min10 videos28 Exercises1,850 XP8,075Statement of accomplishment

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Course Description

Code Smarter with AI

AI is transforming the way developers write, debug, and maintain code. From generating functions to improving documentation and catching bugs, AI coding assistants can dramatically boost productivity—if you know how to prompt them effectively. This course teaches you the art and science of prompt engineering for coding, giving you the tools to harness AI responsibly and efficiently.

Unlock the Power of AI in Development

You’ll begin by exploring how large language models (LLMs) support software development. Learn the fundamentals of prompt engineering, how context shapes AI outputs, and how to iteratively refine prompts for more accurate, reliable results.

Apply Prompt Engineering to Real-World Coding Tasks

Next, you’ll practice advanced prompting strategies for debugging, documentation, and test generation. You’ll also discover how to guide models to improve security, ensuring your AI-assisted workflows avoid common vulnerabilities and support best practices in software development.

Build Robust and Responsible Code

Finally, you’ll evaluate different models to choose the right tool for each task, balance speed, cost, and quality, and apply guardrails to ensure safe and ethical AI use. By the end, you’ll be able to leverage AI-powered coding assistants securely and tailor them to your needs.

Prerequisites

Curriculum

Course outline

1

Unlocking the Power of AI in Code

Learn how AI models enhance coding through completion, debugging, and documentation. Master the basics of prompt engineering and discover how context makes AI responses smarter. This chapter sets the foundation for using AI as a practical coding assistant.
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2

Prompt Engineering for Real-World Coding Tasks

Learn how to craft prompts that guide models through complex reasoning, document code effectively, and generate test-driven implementations. By the end of this chapter, you’ll be able to design prompts that improve both the quality of generated code and your overall development workflow.
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3

Building Robust AI Coding Workflows

Go beyond prompt crafting in this chapter, and into the practical realities of running AI in production. Explore how to compare and select models based on performance, cost, and task fit; how to embed guardrails that keep outputs safe and compliant; and how to fine-tune workflows for efficiency without sacrificing quality.
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AI-Assisted Coding for Developers

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