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.

The AI-assisted coding paradigm

Matching AI models to tasks

Classifying AI workloads

Prompt engineering basics

Decoding functions with the right prompts

Refining prompts for code changes

Adding context for smarter AI responses

Choosing the right context in coding tasks

Improving code with prompt iteration

Unlocking the Power of AI in Code

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.

Advanced prompt engineering for coding assistance

Prompts for effective debugging

Effective role definitions

Documenting code with AI

Optimizing prompts for code documentation

Effective commit messages with AI

Writing tests and securing code with AI

Prompts for secure AI-assisted coding

Spotting gaps in AI-generated tests

Prompt Engineering for Real-World Coding Tasks

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. 

Picking the best AI tool for the job

Understanding the cost of AI assistants

Options

Pros

Cons

Evaluating the trade-offs of reasoning models

Guardrails for responsible AI coding

Security and ethical guardrails for an AI workflow

Applying guardrails

Applying security guardrails to an AI workflow

Optimizing AI for speed, cost and quality

Effective strategies to control model cost

Can use regular (cheaper) models

Requires powerful models

Balancing model performance and cost

Congratulations!

Building Robust AI Coding Workflows

Discover how to supercharge your coding with AI. This course introduces prompt engineering techniques to help you write cleaner code, debug faster, generate documentation, and secure your applications. You’ll learn how to craft precise prompts, choose the right AI tools, and build safe, cost-effective coding workflows. By the end, you’ll be able to integrate AI into real development tasks, streamlining your work. This course is ideal for developers who want to harness AI as a coding partner.

  <h2>Code Smarter with AI</h2>
  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.<br>

  <h2>Unlock the Power of AI in Development</h2>
  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.<br>

  <h2>Apply Prompt Engineering to Real-World Coding Tasks</h2>
  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.<br>

  <h2>Build Robust and Responsible Code</h2>
  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.

Intermediate Python

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

AI-Assisted Coding for Developers

KI für Softwareentwicklung

AI Curriculum Manager, DataCamp

Benchmarks

Model comparison

Tokenization

Cost

Reasoning models

Output quality

Let's practice!

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Building Robust AI Coding Workflows - Picking the best AI tool for the job

Picking the best AI tool for the job

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