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AI Engineering

This section explores the engineering decisions involved in building useful, reliable AI-powered software. It focuses on applying AI to real problems and integrating it into systems people can trust and maintain.

Who this section is for​

  • Engineers and teams designing or building AI-powered products and services.
  • Technical leads evaluating AI approaches, risks and trade-offs.
  • Practitioners responsible for testing, deploying or operating AI-enabled features.

What you'll find here​

  • Guidance on identifying problems where AI can provide practical value.
  • Considerations for selecting models and working with data, prompts and context.
  • Approaches to evaluating quality, reliability and safety beyond a successful demo.
  • Engineering trade-offs involved in integrating and operating AI capabilities in production.

How to use these guides​

  • Start with the problem and its success criteria before choosing an AI technique.
  • Treat evaluation, safety and operational needs as part of the design, not as final checks.
  • Adapt the examples and recommendations to your system's users, constraints and risk level.

The goal is to help teams make deliberate engineering decisions throughout the lifecycle of AI-powered systems, from initial design through production operation.