LLM Engineer Interview Questions: RAG, Prompting, Evaluation and More (2026)

The short answer: LLM engineer interviews focus on building reliable applications on top of large language models: retrieval-augmented generation (RAG), prompting, evaluation, handling hallucinations, choosing between prompting, RAG and fine-tuning, and managing latency, cost and safety. Expect conceptual questions, a design question such as “design a RAG system for our support documents,” and questions about systems you’ve built and how you measured them. Below are the common questions with short answers, grouped by area, and a RAG design walkthrough.

LLM fundamentals

“How does a large language model generate text?”

It predicts the next token based on the tokens so far, one token at a time, using a transformer network trained on large amounts of text. Sampling settings such as temperature control how random the choices are.

“What is a context window?”

The maximum number of tokens the model can consider at once, including the prompt, any retrieved documents and the response. Longer contexts cost more and can dilute attention to the important parts.

“What are embeddings?”

Vectors that represent the meaning of text, so that similar texts are close together. They power semantic search in RAG systems.

Retrieval-augmented generation (RAG)

“What is RAG, and why use it?”

RAG retrieves relevant documents at query time and gives them to the model as context, so answers are grounded in your data. The approach was introduced in a 2020 paper by Lewis and colleagues, which combined a pre-trained model’s “parametric” memory with “non-parametric” memory in the form of retrieved documents. Use it when answers must reflect current or private information, with sources.

“Walk me through a RAG pipeline.”

1. Ingest: load documents, clean them, and split them into chunks.
2. Embed and index: create embeddings for each chunk and store them in a vector index, with metadata.
3. Retrieve: embed the user’s question and find the most similar chunks, often combined with keyword search.
4. Rerank: reorder the top results with a more accurate model.
5. Generate: put the best chunks in the prompt and ask the model to answer using only them, with citations.

“How do you choose a chunk size?”

Big enough to keep the context needed to answer, small enough to be precise. Chunk along natural boundaries (sections, paragraphs), add some overlap, and test different sizes against your evaluation set.

“Why combine keyword and vector search?”

Vector search finds meaning; keyword search finds exact terms such as product codes, names and error messages. Hybrid search usually retrieves better than either alone.

“Retrieval is returning the wrong documents. How do you debug it?”

Check your evaluation set to see which questions fail, inspect the retrieved chunks, and look at chunking, embedding model choice, metadata filters, query rewriting and reranking, one change at a time.

Prompting

“What makes a good prompt?”

Clear instructions, the role and goal, relevant context, the output format, examples where useful, and explicit rules for edge cases, such as what to do when the answer isn’t in the documents.

“How do you get reliable structured output?”

Ask for a defined schema, use the model’s structured output or function-calling features where available, validate the output, and retry or repair when it fails validation.

Evaluation

“How do you evaluate an LLM application?”

Build an evaluation set of realistic inputs with expected outputs or grading criteria. Measure retrieval (did the right documents come back?) and generation (correctness, groundedness in sources, completeness, format). Combine automated checks, model-graded evaluations validated against human judgment, and human review of samples. Run evaluations on every significant change.

“How do you know if your LLM-based grader is trustworthy?”

Compare its scores with human ratings on a sample, look at where they disagree, and refine the grading instructions.

Hallucinations and reliability

“How do you reduce hallucinations?”

Ground answers in retrieved sources, instruct the model to say when it doesn’t know, require citations, lower temperature for factual tasks, check answers against sources, and evaluate groundedness.

Fine-tuning vs RAG vs prompting

“When would you fine-tune instead of using RAG?”

Use prompting first. Use RAG when the model needs knowledge it doesn’t have, especially knowledge that changes. Consider fine-tuning to change behavior, style or format consistently, or to make a smaller model perform a narrow task well. They can be combined.

Production: latency, cost and safety

• Latency: stream responses, cache frequent queries, use smaller models for simple steps, and retrieve in parallel.
• Cost: shorter prompts, fewer retrieved chunks, model routing by task difficulty, and caching.
• Safety and security: guard against prompt injection (especially from retrieved or user-supplied content), restrict what tools the model can call, protect private data, and log and monitor outputs.
• Monitoring: track quality, latency, cost and user feedback in production.

RAG system design walkthrough

“Design an assistant that answers employees’ questions from our internal documentation.”

1. Requirements: who uses it, which documents, how fresh, access permissions, acceptable latency, and how to measure success.
2. Ingestion: connectors for each source, chunking, embeddings, and metadata including permissions; updates when documents change.
3. Retrieval: hybrid search filtered by the user’s permissions, then reranking.
4. Generation: a prompt that requires answers from the retrieved sources with citations, and a clear “I don’t know” path.
5. Evaluation: a test set of real questions, retrieval and answer quality metrics, and human review.
6. Operations: monitoring, feedback buttons, cost controls, and a process to fix bad answers.

See our guide to system design interviews for the general framework.

Interviewing at AI labs

Interviews at companies that build frontier models (such as OpenAI, Anthropic or Google DeepMind) usually go deeper than general LLM engineering roles. Processes differ and change, so check each company’s careers pages, but prepare for:

• Motivation and mission: why this lab, and your views on the benefits and risks of AI. Read the company’s published research, model or system cards and safety commitments, and have a considered opinion.
• Technical depth: coding, ML fundamentals, and for research roles, discussion of papers and your own work in detail.
• Evaluation rigor: how you’d measure a capability or a risk, avoid contamination and overfitting to benchmarks, and know when results are noise.
• Safety and misuse: how you’d think about red-teaming, refusals, harmful outputs and responsible deployment.
• Product judgment: how you’d turn a model capability into something useful and safe for users.

Be honest about what you don’t know; these interviews reward careful reasoning over confident guessing.

Behavioral questions

• Tell me about an LLM feature you shipped. How did you measure quality?
• Tell me about a time a model behaved unexpectedly in production.
• How do you explain the limitations of LLMs to non-technical stakeholders? See explaining something complex.

Frequently asked questions

Sources

• Lewis, P., Perez, E., Piktus, A., et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. arXiv:2005.11401. Read on 1 October 2026.