Interview Q&A / AI & Generative AI
AI & Generative AI interview questions
LLMs, tokens, embeddings, RAG and prompt engineering, the questions every AI role now asks.
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- How does subword tokenization such as byte pair encoding work?
Byte pair encoding starts from small units like characters or bytes and repeatedly merges the most frequent adjacent pair into a new token, until a target vocabulary size is reached. Common words become single tokens, rare words split into pieces.
IntermediateQuick answer1 min read - 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 - Why can the same sentence need more tokens in Hindi than in English?
Tokenizers are trained mostly on the languages that dominate their data, so common English words become single tokens while Hindi words in Devanagari often split into many small pieces. More tokens means higher cost, slower replies and less room in the context window.
IntermediateQuick answer1 min read