Intermediate interview questions in LLMs & Generative AI, page 2
How would you modify retrieval architecture for hybrid text and SQL RAG?
It tests unified retrieval across unstructured text and structured SQL. Outline a query planner that routes to vector search or text-to-SQL, joins the results, and synthesizes a final answer. Never suggest embedding the whole database as text chunks.
Describe a ReAct agent architecture for multi-step dependent tool calls
Sketch ReAct's thought-action-observation cycle; keep state in an append-only trajectory; re-plan after each observation.
What fixes an LLM agent's incorrect JSON arguments for a complex tool?
Tests mixing prompting with system guardrails for valid tool JSON. Outline: few-shot demos plus CoT prompting; schema validation, constrained decoding, and retries. Red flag: weak prompts without validation or structured output.
What is GAN mode collapse, its causes, and two mitigations?
Define mode collapse as diversity loss to few modes; cite discriminator imbalance and lenient JS loss; give two fixes: WGAN and mini-batch discrimination.
How does text guide Stable Diffusion via U-Net cross-attention?
Tests whether you know text embeddings condition the U-Net through cross-attention. Good answers explain that image features query text keys and values at every layer. Red flag: claiming the prompt is concatenated to the image latent.
Evaluating image generation: FID and IS
FID compares feature distributions of real and generated images, lower is better; Inception Score rewards confident, diverse classes but ignores real data.
Designing a Visual Question Answering system
Encode the image with a vision backbone, encode the question with a text encoder, fuse them via cross-attention into a joint representation, then decode or classify the answer.
How Stable Diffusion generates images
The text encoder turns the prompt into embeddings, the U-Net predicts noise to remove conditioned on those embeddings, and the scheduler controls how noise is stepped down over iterations in…
Aligning text and image representations
Contrastive learning like CLIP pulls matched image-text pairs together and pushes mismatches apart; alternatively projection layers map one modality into a frozen model's space.
How FID is calculated versus Inception Score
FID fits Gaussians to Inception features of real and fake images then measures Frechet distance; it uses real references and detects mode collapse.
How to evaluate a RAG system end to end
Measure retrieval (recall, precision, MRR, NDCG) and generation (faithfulness, answer relevance) separately, plus end-to-end correctness.
How MMLU works and the contamination problem
MMLU is multiple-choice across 57 subjects scored by accuracy; contamination means test items leaked into pretraining, inflating scores.
Reference-free evaluation for open-ended dialogue
ROUGE punishes valid paraphrases; use reference-free LLM-as-judge or learned scorers rating coherence, relevance, and groundedness.
Designing input and output guardrails for a chatbot
Input guardrails filter or classify user prompts (injection, off-topic, PII) before the model; output guardrails validate responses for toxicity, leakage, and policy before sending.
Prompt injection versus jailbreak, and defenses
Injection hijacks the model via untrusted data overriding developer instructions; jailbreak coaxes a model past its safety policy. Defense: separate trusted instructions from untrusted data and filter.
PTQ versus QAT for INT8 quantization
PTQ quantizes a trained model with light calibration, fast and cheap but more accuracy loss; QAT simulates quantization during training, higher accuracy but costly.
What memory problem PagedAttention solves
Pre-allocating contiguous max-length cache per sequence wastes memory through internal and external fragmentation; PagedAttention stores KV in fixed non-contiguous blocks like OS paging.
Detecting RAG hallucinations with a confidence score
Decompose the answer into claims, verify each against retrieved context with NLI or an LLM judge, aggregate into a faithfulness confidence score, and flag unsupported claims.
Direct versus indirect injection and agent defenses
Direct injection comes from the user prompt; indirect hides in third-party data the agent ingests like web pages.
Mixture of Experts architecture and routing
Many expert FFNs per layer, a router picks top-k experts per token, only those compute so active params are far fewer than total.
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