The full answer
RAG works in two stages. First, your content is split into passages, converted into numerical representations called embeddings and stored in a vector database such as pgvector or Pinecone. When someone asks a question, the most relevant passages are retrieved and passed to a language model, which writes an answer based on them.
Most of the quality comes from the retrieval step. Creuto pays attention to how documents are split, how metadata such as department, date or product is attached, and how search combines semantic and keyword matching, because poor retrieval produces confident but wrong answers.
Content changes over time, so the system needs a way to stay current. Creuto builds ingestion pipelines that pick up new and updated documents from sources such as shared drives, CMSs or internal tools.
Answers include citations so users can check the original source. Creuto also builds an evaluation set of real questions with known answers and uses it to measure accuracy before launch and after each change.