Applied AI & Gen AI Interview Questions & Answers
Common Applied AI and Generative AI interview questions — LLMs, RAG, agents, embeddings and evaluation — with clear answers. Practise them in a free AI mock interview.
Practise these in a free AI mock interview1. What is RAG (Retrieval-Augmented Generation)?
RAG retrieves relevant documents from a knowledge base (via embeddings + a vector database) and feeds them into the LLM prompt so it answers from your data. It reduces hallucination and uses fresh or private information without retraining.
2. RAG vs fine-tuning — when to use which?
Use RAG when the model needs current or proprietary facts and easy updates. Fine-tune to change style, format or task behaviour that examples can teach. Most systems try RAG first (cheaper, updatable) and fine-tune only when needed.
3. What is an embedding?
A numeric vector that captures the meaning of text (or images) so similar items sit close together in vector space. Embeddings power semantic search and RAG retrieval.
4. How do you reduce hallucinations in an LLM app?
Ground answers with RAG, tell the model to say "I don't know" when unsure, lower temperature for factual tasks, add citations, and validate outputs with schemas/checks or an evaluator. Test on a fixed eval set before shipping.
5. What is the difference between a token and a word?
LLMs process text as tokens — sub-word chunks, so a common word may be one token and a rare word several. Cost and context limits are measured in tokens, not words.
6. What is prompt engineering?
Designing the instructions and context you give an LLM to get reliable output — being specific, giving examples (few-shot), setting a role, requesting a format like JSON, and breaking complex tasks into steps.
7. What is an AI agent?
An LLM that decides and takes actions using tools (search, code, APIs) in a loop to reach a goal, rather than just returning text. Agents plan, call tools, observe results and iterate.
8. How do you evaluate an LLM feature before shipping?
Build a representative test set, define metrics (accuracy, relevance, format-correctness, safety), run automated evals including LLM-as-judge, review edge cases manually, and monitor in production. Never ship on a single good demo.
9. How do you control LLM costs in production?
Use smaller/cheaper models where they suffice, cache repeated calls, trim prompt and context length, cap max tokens, batch requests, and add per-user rate limits.
10. What is a vector database and why use one?
A database optimised to store embeddings and find the nearest (most similar) vectors quickly. It is the retrieval backbone of RAG and semantic search.
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