Data and Search
How a model works with your documents: embeddings, vector databases, RAG and the limits of semantic search. These articles cover what decides whether the assistant finds the right passage.
What is a knowledge base and how a model answers from it
A knowledge base is a company's documents gathered in one place with dates, owners and access rights: the model answers from them and cites its source.
What Semantic Search Is and What It Does for a Business
Semantic search finds documents by the meaning of a query: the system returns the right document even when it contains none of the words from the question.
Contextual chunk enrichment — what it means in plain terms
Contextual chunk enrichment means adding a short explanatory note to every piece of a document so that knowledge base search finds the right one more often.
Embedding weaknesses: four conditions for accurate semantic search
Embedding weaknesses are a known set of places where semantic search misses: exact numbers, outdated revisions, shared access. Each one is fixed by configuration and measurement.
Embeddings explained simply: how machines search by meaning
Embeddings, explained simply, are a way of writing text down as numbers so that phrases with similar meanings end up with similar numbers.
What is RAG: how to make a model answer from your documents
RAG is a way of working with AI where the system first finds the right passage in a company's own documents and then builds an answer from it.
What Is a Vector Database and Why Business Needs It
A vector database stores text and documents as numerical coordinates, so search runs on meaning rather than on matching words.