Semantic Kernel: empower your LLM apps

Semantic Kernel (SK) is an SDK that integrates AI Large Language Models (LLMs) with conventional programming languages.

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Mykhailo Galushko 18 June 2023

What is Semantic Kernel?


Semantic Kernel (SK) is an innovative and lightweight software development kit (SDK) that facilitates the integration of AI Large Language Models (LLMs) with conventional programming languages. The SK extensible programming model combines natural language semantic functions with traditional code native functions, and embeddings-based memory. This powerful combination unlocks new potential and adds value to applications with AI.

The concept of SK has emerged from the need to combine the capabilities of AI Large Language Models (LLMs) with conventional programming languages, without compromising the expressiveness of either. With the SK SDK, developers can now easily harness the power of LLMs in their applications, enabling them to improve the efficiency and accuracy of natural language processing. Furthermore, the SK SDK provides developers with a set of tools that allows them to not only leverage the capabilities of LLMs in their applications, but also to customize and fine-tune these capabilities to suit specific needs. By doing so, developers can create tailor-made solutions that are optimized for their specific use cases, resulting in even more powerful and effective applications. Overall, the SK SDK represents a significant step forward in the field of AI development, and is poised to revolutionize the way we interact with and benefit from this technology.

Why do you need Semantic Kernel?

The Semantic Kernel platform is a highly recommended solution for developers who are seeking to integrate AI into their current applications. With its flexible architecture, SK allows developers to easily add new AI features without the hassle of managing the underlying infrastructure. This platform provides developers with the opportunity to focus on developing their business logic and creating new features, while the platform handles the rest. SK provides a wide range of cutting-edge AI capabilities such as natural language processing, computer vision, and many more. Additionally, this platform is open-source, providing developers with the freedom to customize and tailor the platform to meet their needs.

Key features of Semantic Kernel

The key features of SK include its extensibility, which allows developers to add new natural language semantic functions to the system; its embeddings-based memory, which enables the system to remember context and relationships between concepts; and its support for chaining, which allows multiple semantic functions to be combined in a single operation.

Core features:

Semantic Kernel supports C# and Python.

Use cases

SK has a lot of use cases and scenarios, like building complex user flows and pipelines powered with LLMs.

Common use cases:

The most powerful SK alternative is LangChain ( LangChain has many more integrations and plugins for now. But if you focus on .NET area — Semantic Kernel is a great solution for you to build LLM applications.


Let’s build an app that allows to ask questions to our PDF files. We will use a popular test sample for querying PDF files — bitcoin.pdf.

Source code:

Potential improvements:



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