Preface

Artificial intelligence is experiencing unprecedented growth, with new models emerging daily. With over 20 years in the technology sector, I can attest that the pace of innovation has never been this fast. This brings not only opportunities but also considerable change. Navigating these changes can be challenging and costly, as you may invest a lot of time learning a new technology that might become obsolete.

Enter Microsoft Semantic Kernel – a framework that reduces these risks by enabling access to various AI services through popular programming languages. This framework spares you the details of grappling with constantly evolving APIs. By learning Microsoft Semantic Kernel, you can write code at the framework level, and the framework will call the underlying models for you. This allows you to focus on core concepts instead of the details of each model.

One of the key benefits of Semantic Kernel is its ability to use different AI services. For example, code initially targeting the OpenAI GPT platform can be switched to Google Gemini, often without any modifications. This flexibility makes it easier to integrate AI into applications and to make minimal modifications to them when change inevitably happens.

Moreover, Semantic Kernel makes AI accessible to enterprise programming languages. While Python has long dominated the AI landscape, many enterprise applications rely on C# or Java. Recognizing this, Semantic Kernel not only supports Python but also elevates C# to a first-class AI language. Java support is currently in its beta stages and is expected to launch fully in 2024.

Whether you’re a solo developer or part of a larger enterprise, the demand to add AI functionality to applications is inevitable. This book was created to equip you with the necessary skills to implement AI quickly and effectively, ensuring you are well-prepared to meet this growing demand.