# Metadata Source URL:: https://weaviate.io/developers/weaviate/modules/reader-generator-modules/qna-openai Topics:: #ai, #embeddings --- # Question Answering - OpenAI | Weaviate - vector search engine In short ## Highlights > [!quote]+ Updated on 280123_131606 > > First it performs a semantic search with k=1 to find the document (e.g. a Sentence, Paragraph, Article, etc.) which is most likely to contain the answer. This step has no certainty threshold and as long as at least one document is present, it will be fetched and selected as the one most likely containing the answer. In a second step, Weaviate creates the required prompt as an input to an external call made to the OpenAI Completions endpoint. Weaviate uses the most relevant documents to establish a prompt for which OpenAI extracts the answer