The New Standard for AI Interviews
Based on the current landscape of AI engineering (2024-2025), the bar has been raised. Companies are looking for engineers who understand Generative AI, RAG pipelines, and Production LLM deployment.
These 15 questions cover Foundational Architecture, RAG Systems, Fine-Tuning, and Production Engineering, tailored for candidates with a backend and cloud focus.
I. LLM Architecture & Foundations
1. Explain the Self-Attention mechanism. What are Query, Key, and Value vectors?
Answer: Self-attention enables Transformers to weigh the importance of different words in a sequence relative to each other, capturing long-range dependencies.
- Concept: For every token, the model calculates "attention" scores for every other token.
- Q, K, V Vectors:
- Query (Q): The current token looking for information (like a search query).
- Key (K): The content of other tokens (like database keys).
- Value (V): The actual information content.
- Process: Dot product of Q and K determines relevance scores (normalized via Softmax). These weights multiply V to produce the final representation.
2. What is Tokenization, and how does it impact performance/cost?
Answer: Converting raw text into numerical tokens (sub-word units via BPE/Byte-Pair Encoding).
- Performance: Poor tokenization fails on rare words/code. Efficient tokenizers compress text better, fitting more into the context window.
- Cost: APIs charge per million tokens. Inefficient splitting = higher costs and latency.
3. What are "Hallucinations" and how do you mitigate them?
Answer: When LLMs confidently generate factually incorrect info (probabilistic prediction errors).
Mitigation Strategies:
- RAG: Ground responses in verified external data.
- Prompt Engineering: Instructions like "Answer only using provided context."
- Temperature: Lowering (e.g., 0.0) favors deterministic outputs.
- Chain-of-Thought (CoT): Step-by-step reasoning reduces logic errors.
II. Retrieval-Augmented Generation (RAG)
4. Explain the end-to-end RAG architecture.
Answer: Two main pipelines:
- Ingestion: Load Docs → Chunk (split text) → Embed (convert to vectors) → Store (Vector DB like Pinecone).
- Retrieval & Generation: User Query → Embed Query → Semantic Search (Cosine Similarity) → Augment Prompt with Context → LLM Generation.
5. Dense vs. Sparse Retrieval? When to use Hybrid Search?
- Sparse (BM25): Matches exact keywords. Fast, explainable, but misses synonyms.
- Dense (Vector): Matches semantic meaning ("canine" matches "dog"). Requires embeddings.
- Hybrid Search: Combines both. Necessary because Dense misses specific IDs (like Product SKUs) that Keyword search catches. Often uses Re-ranking (Cross-Encoder) to merge results.
6. Chunking Strategies & Retrieval Quality
- Fixed-Size: Simple split every N tokens. Can break sentences.
- Recursive: Splits by paragraphs first, then sentences. Standard in LangChain.
- Semantic: Splits based on meaning changes. Computational expensive.
- Trade-off: Small chunks = precise retrieval, low context. Large chunks = high context, more noise.
7. How do you evaluate a RAG system?
Answer: Use frameworks like RAGAS or TruLens.
- Retrieval Metrics: Hit Rate (Recall), MRR (Rank).
- Generation Metrics: Faithfulness (no hallucinations), Answer Relevance (addressed query?).
Resume Check: Does your resume highlight "RAG Evaluation" or "Hybrid Search"? If not, you might be filtered out. Scan your resume for AI keywords now.
III. Fine-Tuning & Optimization
8. RAG vs. Fine-Tuning?
- Use RAG: For frequent data changes (news), factual grounding, citations.
- Use Fine-Tuning: For changing behavior/style (e.g., speak like a pirate), output formatting (JSON), or deep domain language patterns.
- Hybrid: Fine-tune a small model to be a better RAG reasoner.
9. What is PEFT and LoRA?
Answer: Parameter-Efficient Fine-Tuning.
- LoRA (Low-Rank Adaptation): Injects small rank decomposition matrices (A, B) into layers. Only trains these adapter weights, freezing the base model.
- Benefit: Reduces trainable params by 99%, enabling training 70B models on consumer GPUs.
10. What is Quantization (QLoRA)?
Answer: Reducing weight precision (16-bit → 4-bit).
- Impact: 4-bit models use 4x less VRAM.
- QLoRA: Freezes base model in 4-bit while training LoRA adapters in 16-bit.
IV. System Design & Production
11. Handling Context Limits & "Lost in the Middle"?
- Context Limits: Use Map-Reduce (summarize docs) or Refine strategies.
- Lost in the Middle: LLMs ignore middle context. Fix: Re-rank relevant docs to the start/end of the prompt.
12. How to optimize GenAI latency?
- Semantic Caching (GPTCache): Return cached answers for similar queries.
- Streaming: Token-by-token response for perceived speed.
- Smaller Models: Route simple queries to Llama-8B/GPT-4o-mini.
- Parallel Retrieval: Query vector DB and APIs concurrently.
13. LLM vs. Agent?
LLM: Passive engine. Input → Output.
Agent: LLM + Tools + Loop. Observes task → Decides tool (Search, Calculator) → Executes → Repeats (e.g., ReAct pattern).
14. What is RLHF?
Answer: Reinforcement Learning from Human Feedback. Turns raw predictor into helpful Chatbot.
- SFT: Supervised Fine-Tuning on Q&A.
- Reward Model: Predicts human preference.
- PPO: Optimizes LLM to maximize reward score.
15. Explain Chain-of-Thought (CoT).
Answer: Prompting model to generate intermediate reasoning steps ("Let's think step by step").
Mechanism: Generating reasoning tokens "buys compute time" to resolve logic dependencies before the final answer.
Are You Qualified for these Roles?
Knowing the answers is step one. Step two is proving you have the experience. Check if your resume contains the Production AI Keywords that hiring managers filter for.