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 ofAI models andHow-Tos 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, seeEmbeddings.

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 calledTool 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, seeTool Calling.

If you want to see our AI Model Hub in action, seeHow-Tos!

Last updated2 months ago