In brief
Embeddings are mathematical representations of text. Rather than simply comparing words literally, a system can use embeddings to identify which pieces of content are similar in meaning. This makes it easier to identify meaning and relevance.
Why are embeddings necessary?
Without embeddings, search often relies primarily on exact matches. This is limiting, as people can phrase the same question in many different ways. Embeddings help a system to recognise similarities in content, even when the words used differ.
How are they used in RAG and search systems?
Documents are broken down into smaller sections, which are then converted into vectors. When a new question is asked, the system also creates a vector of the question and searches for text fragments that are thematically similar. This allows it to retrieve relevant context before the model provides an answer.
What determines quality?
The quality depends on the embedding models used, how documents are segmented, how up-to-date the source data is and how the search layer is configured. Good embeddings do not compensate for a poor source structure. They work best when the entire knowledge chain is sound.
What do you need to remember?
Embeddings do not provide answers in their own right. They primarily help to find the right information. Think of them as a technical foundation beneath knowledge bases, search functions and RAG systems, rather than as a standalone end solution.
From concept to practice
How AITJE applies this
For searching a knowledge base, we use the concept behind embeddings to find relevant document passages that match a query. During the setup, we assess which search approach suits your sources, language and document structure. The passages found remain linked to the original documents and access rights. Whenever the index or the embedding model is changed, we retest whether the key queries still return the correct information.
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