Interview Q&A / AI & Generative AI

1. What are embeddings, and how is vector similarity used to compare text?

   An embedding is a list of numbers that represents the meaning of a piece of text, so texts with similar meaning get vectors that point in similar directions. Similarity measures such as cosine similarity compare these vectors to find related text.

   IntermediateIn-depth3 min read
2. What is retrieval-augmented generation (RAG), and when would you use it?

   RAG first retrieves relevant passages from your own data, then gives them to the LLM along with the question so it answers from that material. Use it when answers must come from private, large or frequently changing information the model was not trained on.

   IntermediateIn-depth3 min read
3. Why must you use the same embedding model for documents and queries?

   Each embedding model builds its own vector space, with its own dimensions and meaning for every number. Vectors from different models are not comparable, so mixing them gives meaningless similarity scores, or fails if the sizes differ.

   BeginnerQuick answer1 min read
4. What is the difference between cosine similarity, dot product and Euclidean distance for vectors?

   Cosine similarity compares only direction, dot product mixes direction and length, and Euclidean distance measures straight-line gap. For normalised vectors, cosine and dot product give the same ranking, and Euclidean distance ranks the same way too.

   IntermediateQuick answer1 min read
5. What is the difference between token embeddings and sentence embeddings?

   A token embedding is the vector for one token that goes into the model's layers. A sentence embedding is a single vector summarising a whole text, produced by an embedding model, and used for search, clustering and similarity.

   IntermediateQuick answer1 min read