Advanced Concepts

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At IONOS, we believe AI is transforming the way we interact with technology and each other. Through our AI Model Hub, we offer a variety of AI features and use case documentation to help you get started quickly.

We introduce several advanced concepts to help you better understand and apply AI technologies.

Concept 1: Embeddings

Embeddings are layers in deep learning models representing data in a lower-dimensional space. Although they do not make the final prediction themselves, embeddings can help identify semantically similar objects — crucial for tasks such as recommendation, clustering, and search.

Key learnings

• Understand the basic idea behind embeddings.

• Learn how to use embeddings to measure semantic similarity.

For more information, see Embeddings.

Concept 2: Tool Calling

Large Language Models are trained on historical data, so their built-in knowledge ends at a specific cutoff point. To extend their capabilities, you can use a technique called Tool Calling.

Tool calling allows a model to access external tools—such as APIs, databases, or custom functions—at runtime. Developers define the available tools in the model's prompt, and the model learns to decide when to use a tool versus when to answer based on its internal knowledge. This enables language models to work with up-to-date, private, or computational information they couldn't otherwise access.

Key learnings

• Understand how tool calling extends a model’s functionality beyond its training data.

• Learn what tool calling enables and where its limitations lie.

For more information, see Tool Calling.

If you want to see our AI Model Hub in action, use our Tutorials or Use Cases!