YOUR TRACK · 2 QUESTIONS
Understand language models, retrieval, and the systems built around them.
Interview-ready answer
A large language model is a neural network trained on large collections of text to predict likely next tokens. When given a prompt, it converts the text into tokens, uses a Transformer to process their context, and generates a response one token at a time. It learns patterns rather than checking every statement against a trusted source, so its answers can sound plausible and still be wrong.
Interview-ready answer
Retrieval-Augmented Generation (RAG) retrieves relevant material from an external knowledge source and supplies it to a language model alongside the user’s question. A typical system chunks and indexes documents, retrieves likely matches at query time, and asks the model to answer using that context. RAG is useful when answers should reflect private, changing, or cited information, but retrieval quality and source quality still limit the result.