2. LLM Interview Questions

🏗️ Core Architecture & RAG

1. What is LLM System Design, and why is it important?

Answer: LLM System Design refers to the end-to-end architecture for deploying large language models in production. It encompasses:

• Infrastructure: Hardware (GPU/TPU), cloud providers, and optimization.
• Inference Pipelines: Latency reduction, caching, and batching strategies.
• Integration: APIs, Retrieval-Augmented Generation (RAG), and safety guardrails.
• Scalability: Managing high traffic while balancing cost-performance trade-offs.

Importance: Without robust design, LLMs suffer from high latency, "hallucinations," security vulnerabilities, and unsustainable operational costs.

2. Explain Retrieval-Augmented Generation (RAG).

Answer: RAG enhances LLM outputs by fetching external, real-time data to ground responses in facts.

• The Workflow:
• Embed: Convert user query into a vector (e.g., via SentenceTransformers).
• Retrieve: Find top-k relevant document chunks from a Vector DB (e.g., Pinecone, Milvus).
• Augment: Insert those chunks into the prompt as "context."

• Generate: The LLM answers based strictly on the provided context.

• Benefits: Reduces hallucinations, provides a clear audit trail (citations), and allows for "updating" model knowledge without expensive retraining.

⚡ Performance Optimization

3. How do you optimize for low latency and high throughput?

Answer:

• Quantization: Reducing weight precision (e.g., FP16 to INT8 or 4-bit) to save VRAM and speed up compute.
• KV Caching: Storing previous "Key-Value" pairs in the attention mechanism to avoid recomputing the entire prompt prefix.
• Speculative Decoding: Using a tiny "draft" model to predict tokens, which are then verified in parallel by the large "target" model.
• Continuous Batching: Processing multiple requests simultaneously using frameworks like vLLM or TGI.

4. Compare Cloud vs. Edge Deployment.

🛠️ Reliability & Operations

5. Describe key Non-Functional Requirements (NFRs).

Answer:

• Latency: Aim for <200ms Time-To-First-Token (TTFT) for interactive chat.
• Cost: Implement "Model Cascading"—use GPT-4 for logic, but GPT-4o-mini or Mistral for classification.
• Safety: Use toxicity filters (Perspective API) and PII redaction (Presidio).
• Observability: Track Perplexity (model confidence) and Token Usage (budgeting).

6. How do you handle LLM Hallucinations?

Answer:

• Self-Correction: Ask the LLM to review its own response for factual errors.
• N-Step Verification: Use a second, smaller model to cross-reference the output against the retrieved RAG source.
• Temperature Control: Lowering temperature (e.g., to 0.1) makes the model more deterministic and less "creative."

💼 Product & Case Studies

7. Design Case Study: "Chat with PDF"

Answer:

• Ingestion: Parse PDFs using Unstructured.io; use recursive character splitting to create chunks (e.g., 512 tokens with 10% overlap).
• Storage: Store embeddings in a Vector DB like ChromaDB or Weaviate.
• Retrieval: Use Hybrid Search (combining Vector/Semantic search with Keyword/BM25 search) to find specific names or dates.
• UX: Implement Streaming (Server-Sent Events) so the user sees text as it generates rather than waiting 10 seconds.

8. How do you measure LLM ROI?

Answer:

• Deflection Rate: Percentage of support tickets resolved without human intervention.
• Agent Assist: Reduction in "Average Handle Time" (AHT) for human agents using LLM drafting tools.
• Accuracy vs. Cost: Measuring if a 10% increase in model accuracy justifies a 5x increase in API costs.

🛡️ Ethics & Safety

9. What are the ethical risks of LLM deployment?

Answer:

• Bias: Training data may reflect historical prejudices; requires "Red Teaming" to identify and mitigate.
• Data Leakage: Users might input sensitive company data into public models. Solution: Use VPC-isolated deployments or PII scrubbing.
• Over-reliance: Users treating the LLM as a source of truth for legal or medical advice. Solution: Hardcoded disclaimers and "human-in-the-loop" workflows.