GenAI Basics · Question 26 · Medium
In a RAG system, what is the main purpose of a vector database?
- Storing embeddings and quickly finding the vectors most similar to a query vector
- Storing the model's weights during training
- Translating text between languages
- Compressing images for faster upload
Answer and explanation
Answer: A. Storing embeddings and quickly finding the vectors most similar to a query vector
Explanation: Document chunks are stored as embedding vectors. At query time the query is embedded, and the vector database returns the nearest chunks, using measures such as cosine similarity.
Previous: An AI email assistant summarises incoming emails. One email contains hidden… · Next: A query embedding is q = (1, 2, 2). Four document embeddings are: D1 = (2, 1…
Practise genai basics
Try a fresh set of genai basics questions with an explanation after every answer, free and without signing up: GenAI Basics practice. Or sit a full placement mock test.
More genai basics questions
- Assume that, on average, one token corresponds to about 0.75 English words…
- What is chain-of-thought prompting?
- An AI email assistant summarises incoming emails. One email contains hidden…
- A query embedding is q = (1, 2, 2). Four document embeddings are: D1 = (2, 1…
- A model's two candidate next tokens have logits 2 and 0. With softmax at…
- In Reinforcement Learning from Human Feedback (RLHF), what is the reward model…
- Why do transformer models add positional encodings (or positional embeddings)…
More technical & programming practice
Pseudo-code · C Programming · Java · Python · Data Structures · Algorithms · Object-Oriented Programming · DBMS
LogicWiz Exams home · All practice topics · Placement mock tests · Daily 10 challenge