In recent years, the standards for hiring AI talent have changed a lot. Leading companies like MAANG now want engineers who understand system trade-offs, edge cases, and optimization, not just those who can call an API. If you’re getting ready for your next role, you’ll need to go beyond the basics and build a strong system design mindset to master LLM and RAG interview questions.
In this article, I’ve gathered three valuable problem scenarios based on real production challenges. I’ll walk you through how to tackle each one step by step, just like you would in a real interview.
LLM and RAG Interview Questions
Information Density vs. Lost in the Middle
In Large Language Model systems, how you split documents into chunks affects how well information can be retrieved. Chunks that are too small lose important context, while chunks that are too large add noise and can hit context window limits. This can also cause the “lost in the middle” problem, where LLMs miss information hidden in the middle of a long prompt.
The Interview Question:
We are building a RAG system for internal legal documents. Some documents are 100 pages long and contain highly specific clauses. When using a fixed chunk size of 512 tokens with 10% overlap, we observe that queries for specific multi-page cross-references fail consistently. How would you redesign the ingestion and chunking strategy to fix this without overflowing the context window?
Here’s how you can tackle this question in an interview:
- First, point out the problem. Fixed-size chunking can split up important sections, like legal clauses or tables, across different chunks. A 10% overlap isn’t enough if a cross-reference goes over several pages.
- Next, suggest using parent-child (hierarchical) chunking. This means you separate the chunk used for searching from the chunk you actually send to the LLM.
- When a child chunk matches the query, you then retrieve its parent chunk and use that as input for the LLM.
Here’s an example implementation using Python:
# Conceptual framework for a Parent-Child Retrieval Strategy
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_core.documents import Document
def create_hierarchical_chunks(raw_documents):
# Large parent chunks to preserve legal context
parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=200)
# Small child chunks for high-fidelity vector matching
child_splitter = RecursiveCharacterTextSplitter(chunk_size=256, chunk_overlap=30)
docs_to_index = []
for doc in raw_documents:
parent_docs = parent_splitter.split_documents([doc])
for p_idx, p_doc in enumerate(parent_docs):
child_docs = child_splitter.split_documents([p_doc])
for c_doc in child_docs:
# Store parent context reference inside child metadata
c_doc.metadata["parent_id"] = f"doc_{p_idx}"
c_doc.metadata["parent_content"] = p_doc.page_content
docs_to_index.append(c_doc)
return docs_to_indexBuild the practical GenAI skills interviewers look for with Hands-On GenAI, LLMs & AI Agents.
The RAG Triad
You can’t improve what you don’t measure. It’s tough to evaluate an LLM application because its language outputs are open-ended. To reliably assess a RAG pipeline without manual grading, use the “RAG Triad”: Faithfulness, Answer Relevance, and Context Relevance.
The Interview Question:
Your production RAG system is experiencing user complaints about ‘hallucinations.’ How would you set up an automated, programmatic evaluation pipeline to track down whether the root cause lies in poor retrieval or poor generation?
To answer in a structured way, organize your metrics into a clear, actionable matrix:
| Metric | Target Evaluation | How it is Computed (LLM-as-a-Judge) | If Low, How to Fix |
|---|---|---|---|
| Context Precision | Retrieval Quality | Does the retrieved context contain only relevant info? | Optimize vector search, use a Re-ranker (e.g., Cohere Rerank). |
| Faithfulness | Generation Quality | Is the generated answer grounded strictly in the context? | Adjust system prompt, turn down temperature, increase context quality. |
| Answer Relevance | Combined Quality | Does the generated response directly answer the user’s prompt? | Refine prompt engineering, enforce formatting guardrails. |
By calculating these three scores on a test dataset, you can find where the problem is. For example, if Faithfulness is high but Answer Relevance is low, the model isn’t hallucinating, it’s just not answering the query correctly.
Deterministic Validation on Non-Deterministic Outputs
LLMs create text based on probability, so they don’t actually know what’s true or false. Guardrails are programmatic checks that wrap around your model’s inputs and outputs to make sure the text is safe, follows format rules, and is factually correct before users see it.
The Interview Question:
You are deploying a customer-facing financial advice bot using an LLM. You cannot afford a single hallucinated interest rate or numerical error. How do you design a real-time guardrail layer to intercept and validate outputs before they reach the user?
Here’s how you can handle this question in an interview:
- Start with layered defense. Don’t just rely on prompt instructions like “Do not lie.” You need to add programmatic validation layers using tools such as NeMo Guardrails or Guardrails AI.
- Stage 1: Check the generated response for important values, like percentages or dollar amounts, using regular expressions.
- Stage 2: Programmatically compare these extracted values with your internal SQL database or source documents to make sure they match.
- If you find a mismatch, stop the output and show a safe fallback message: “I cannot verify this specific rate right now. Please consult our official rates page.”
Closing Thoughts
When you’re in an AI or system design interview, keep in mind that there’s usually more than one right answer. Interviewers want to see how you weigh trade-offs. For example, making chunks bigger gives more context but also increases latency and costs. Fine-tuning an embedding model can boost accuracy but adds a lot of engineering work.
If you can clearly explain these engineering trade-offs and support your choices with solid system principles, you’ll stand out in your interview. Stay curious, focus on data engineering in AI pipelines, and keep building your skills.
I hope you found this article on LLM and RAG interview questions helpful.
For more AI and machine learning tips, follow me on Instagram. My book, Hands-On GenAI, LLMs & AI Agents, can also help you grow your AI career.
