Technology - 6 min
Embeddings turn text into vectors so systems can recognize which pieces of information are semantically similar. They are a key technical foundation for semantic search and many RAG-based systems.
Embeddings are mathematical representations of text. Instead of comparing only exact words, a system can use embeddings to see which pieces of content are close in meaning. That makes relevance and semantic similarity much easier to work with.
Without embeddings, search often relies heavily on exact wording. That is limiting because people ask the same question in many different ways. Embeddings help a system understand related meaning even when the phrasing is different.
Documents are usually split into smaller chunks and turned into vectors. When a new question comes in, the system also creates a vector for that question and looks for text fragments that are semantically close. That is how relevant context is retrieved before the model answers.
Quality depends on the embedding model, how documents are chunked, how current the source data is and how the retrieval layer is set up. Good embeddings do not fix weak source material. They work best when the entire knowledge pipeline is solid.
Embeddings do not answer questions by themselves. Their main role is to help find the right information. Think of them as a technical foundation under knowledge bases, search flows and RAG systems, not as a complete solution on their own.
Continue reading
An LLM is a large language model that can understand, predict and generate text. It often feels smart, but the quality of the result still depends heavily on context, model choice and how you use it.
Open article
Edge AI means AI runs close to the source of the data, for example on a device, server or local network. This can improve speed and often gives more control over privacy, continuity and cost.
Open article
RAG is a way to let a language model retrieve relevant information from documents or a knowledge base before it answers. That usually leads to answers that are more specific, grounded and useful for your own organization.
Open article
Next step
If you want to know whether AITJE Assistent, AITJE Custom or a future product direction fits your organization, we can go through that with you directly.