Generative AI with Large Language Models - Interview Questions and Answers with Solved Quiz Questions

In this post, I explain Introduction to Generative AI with Large Language Models, Key Concepts & Definitions, Underlying Models: Transformers & Beyond, Modeling andTraining Foundations, Sampling & Decoding for Generation Quality, Prompting Strategies for Generative AI (zero-shot, few-shot, chain-of-thought prompting, role prompting, and advanced prompt tactics), Scaling & Emergent Capabilities in Generation, Mitigating Hallucination & Ensuring Output Reliability -RAG and grounding, and Advanced Generation: Multimodality & Specialized Content. If you want my full Gen AI with LLMs document also including the following topics, you can use the Contact Form (in the right pane) or message me in LinkedIn:
Popular Generative LLMs & Frameworks (GPT-series, Claude, PaLM, Gemini, LLaMA), Efficiency & Deployment Optimization distillation, quantization, parameter-efficient tuning etc.), Ethics, Privacy & Governance, Generative AI Project Workflow (end-to-end lifecycle), Practical Use Cases (chatbots, summarization, #code generation, interactive Q&A, and RAG systems) and Interview Preparation & Consolidated Quiz.

Question: What is generative AI with large language models? How does Gen AI differ from other AI paradigms?
Answer: Generative AI (Gen AI) with large language models means the use of massively scaled transformer-based neural networks that can produce new, coherent text by predicting sequences of tokens. Unlike discriminative models that classify or score inputs (e.g., image classifiers or sentiment detectors), generative LLMs create original content, ranging from essays to code, by sampling from learned probability distributions over language. This enables open-ended applications such as creative writing, dialogue systems, and automated report generation.

Question: What is the scope of generative AI with LLMs? When is it appropriate to use Gen AI?
Answer: The scope includes tasks where language generation, completion, or transformation is needed: drafting emails, summarizing documents, translating languages, writing code, or crafting conversational agents. It is most appropriate when human-level fluency, adaptability, and contextual understanding are required at scale, and when the cost or latency of manual creation is prohibitive. However, for tasks demanding strictly verifiable facts or precise numeric calculations, hybrid approaches, combining retrieval or symbolic modules with LLM generation, often improve reliability.
Example: A marketing team uses a generative AI with an LLM to produce multiple slogan drafts in seconds, selecting and refining the most brand-aligned options rather than starting from a blank page.

Quiz
1. Which characteristic distinguishes generative AI with LLMs from discriminative AI? 
A. Predicts class labels for given inputs
B. Generates new sequences of tokens (Correct)
C. Requires labeled training data exclusively
D. Operates only on numeric data

2. In which scenario is generative AI with large language models most appropriate?
A. Sorting images into predefined categories 
B. Generating a first draft of a legal brief (Correct)
C. Performing high-precision arithmetic operations
D. Monitoring real-time sensor data for anomalies

3. When might a hybrid approach be preferred over pure LLM generation?
A. When generating creative poetry
B. When translating conversational text 
C. When requiring strictly verifiable facts (Correct)
D. When producing casual social media posts

Question: What is a large language model? How does LLM relate to a foundation model?
Answer: A large language model (LLM) is a neural network, typically transformer-based, trained on vast text corpora to learn statistical patterns of language and generate coherent text. A foundation model is a broader category encompassing any massive pre-trained model (text, image, or multimodal), that serves as a base for fine-tuning across downstream tasks. In practice, a large language model like GPT-4 is a text-centric foundation model that can be adapted to various applications such as translation, summarization, or code generation by fine-tuning or prompting.

Question: What is generative AI? What do LLMs stand for?
Answer: Generative AI refers to algorithms designed to create new content (such as text, images, audio, or code) by learning underlying data distributions. LLMs stands for Large Language Models, highlighting both the model’s focus on language and its extensive scale, often having billions or trillions of parameters. Together, generative AI with LLMs uses these heavyweight text generators to produce human-quality language artifacts.

Question: Can you provide a generative AI with large language models example scenario?
Answer: Example: A legal research team needs concise summaries of recent case law on data privacy. They feed full-text opinions into a generative AI system powered by an LLM, prompting: "Summarize the key holding and rationale in three bullet points." The model returns targeted summaries that capture legal precedents and reasoning, enabling attorneys to review dozens of cases in minutes rather than hours.

Quiz
1. Which term describes a massive pre-trained model adaptable to multiple downstream tasks?
A. Discriminative model
B. Foundation model (Correct)
C. Convolutional network
D. Autoencoder

2. What does LLMs stand for?
A. Low-Level Metrics
B. Large Language Models (Correct)
C. Layered Learning Modules
D. Logic-Linguistic Machines

3. In the example scenario provided above, what is the main benefit of using generative AI with an LLM?
A. Automating code compilation
B. Speeding up case law review by producing concise summaries (Correct)
C. Detecting anomalies in network traffic
D. Classifying images into predefined categories

Question: What is the transformer architecture? Why is the transformer architecture it foundational for generative AI?
Answer: The transformer architecture is a neural network design that processes entire token sequences in parallel using attention mechanisms instead of recurrence. It consists of stacked layers that alternate between self-attention and position-wise feed-forward networks, wrapped in residual connections and layer normalization. This parallelism allows transformers to capture both local and global dependencies efficiently, making them ideal for large-scale text generation where context length and coherence are paramount.

Question: How do the encoder, decoder, and self-attention components collaborate in a transformer?
Answer: The encoder transforms an input token sequence into rich contextual embeddings by applying multiple layers of self-attention, which computes relationships among all tokens, and feed-forward networks to each position independently. In an encoder-decoder setup, the decoder then generates output tokens autoregressively. Each decoder layer first applies self-attention over previously generated tokens to maintain consistency, then cross-attention over encoder outputs to ground generation in the input context, and finally a feed-forward network. Self-attention projects each token embedding into queries, keys, and values, using scaled dot-product attention to weight and combining values from all positions.

Question: How are transformers adapted specifically for text generation tasks in generative AI?
Answer: For pure text generation, models often use a decoder-only transformer, where each layer’s self-attention is masked to prevent tokens from attending future positions. This autoregressive masking ensures that each predicted token relies only on prior context. During inference, the model samples the next token from the output distribution (using techniques like top-k or nucleus sampling) and appends it to the sequence, repeating until completion. Such adaptations enable fluent, coherent generation for applications like dialogue, summarization, and story writing.
Example: A decoder-only transformer receives the prompt “Draft an email confirming the meeting.” It uses masked self-attention to generate “Dear team, I’m writing to confirm our meeting scheduled for Monday at 10 AM…” one token at a time, maintaining context and grammatical structure throughout.

Quiz
1. Which mechanism allows transformers to weigh the influence of all tokens in a sequence when encoding each position?
A. Convolutional layers
B. Self-attention (Correct)
C. Recurrent connections
D. Max pooling

2. In a decoder-only transformer used for text generation, why is masking applied during self-attention?
A. To normalize token embeddings
B. To allow bidirectional context
C. To prevent tokens from attending to future positions (Correct)
D. To reduce model size

3. What is the main advantage of the transformer’s parallel processing over recurrent architectures?
A. Lower memory usage
B. Faster capture of both local and global dependencies (Correct)
C. Simpler implementation
D. Deterministic output sequences

Question: What is pre-training and how does self-supervised learning work in LLM development?
Answer: Pre-training is the initial phase where a model ingests vast unlabeled text corpora to learn general language patterns by predicting missing or next tokens. This uses self supervised learning, meaning the data itself provides training signals (for example, randomly masking 15 % of tokens in a sentence and training the model to recover them). Over countless examples, the LLM internalizes syntax, semantics, and factual associations without human annotations.
Example: During pre-training, the model sees "The research on [MASK] AI has expanded rapidly" and learns to predict "generative" by using context learned earlier from millions of similar sentences.

Question: What is fine-tuning and how does it adapt an LLM to specific tasks?
Answer: Fine-tuning takes a pre-trained LLM and trains it on a smaller, labeled dataset tailored to a target task, such as sentiment analysis or medical coding. By adjusting the model’s parameters on domain-specific examples, it tunes its ability to perform that task with higher accuracy and reduced hallucinations. Fine-tuning connects broad language understanding with precise, specialized outputs.
Example: A customer-support LLM fine-tuned on transcripts of product inquiries learns to classify tickets by issue type and generate consistent resolution templates.

Question: What is instruction tuning and why is it important?
Answer: Instruction tuning further refines an LLM by training it on natural-language instructions paired with desired outputs. Unlike traditional fine-tuning, which focuses on input–output examples, instruction tuning teaches the model to interpret and follow arbitrary human readable directives, enhancing its flexibility and zero-shot performance on new tasks.
Example: An instruction-tuned model learns to execute "Translate the following paragraph into French" or "Summarize this article in two sentences" without requiring task-specific fine-tuning.

Question: What is reinforcement learning from human feedback (RLHF) and how does it improve generative quality?
Answer: RLHF combines reinforcement learning with human judgments to align the LLM’s behavior with user preferences. After pre-training and instruction tuning, the model generates outputs that human users rank; these rankings train a reward model. The LLM policy is then optimized via reinforcement signals to maximize this reward, producing responses that are more helpful, factual, and engaging.
Example: A dialogue LLM using RLHF learns to avoid overly verbose or off-topic answers because human raters penalize such outputs during reward-model training.

Quiz
1. What is true about self-supervised learning during pre-training?
A. Reliance on large labeled datasets
B. Generating training signals from the data itself (Correct)
C. Use of reinforcement learning methods
D. Exclusive fine-tuning on downstream tasks

2. Which process best adapts a broad LLM to excel at a single, well-defined task?
A. Pre-training
B. Instruction tuning
C. Fine-tuning (Correct)
D. RLHF

3. Why is instruction tuning more flexible than standard fine-tuning?
A. It reduces model size
B. It uses reinforcement learning
C. It teaches the model to follow arbitrary human-readable directives (Correct)
D. It only masks tokens during training

4. In RLHF, what role do human judgments play?
A. They label the dataset for supervised learning
B. They train a reward model that guides policy optimization (Correct)
C. They replace pre-training entirely
D. They perform gradient updates directly on the LLM

Question: What is top-k sampling and how does it affect generation diversity?
Answer: Top-k sampling constrains the next-token selection to the k tokens with the highest probabilities, then samples from that trimmed distribution. By excluding low-probability tokens, it prevents unlikely words from emerging, balancing coherence with controlled randomness. Smaller k yields more predictable output; larger k increases creativity at the cost of potential incoherence.
Example: If k = 5 and the model’s sorted probabilities for next words are "the" (0.25), "a" (0.20), "this" (0.15), "our" (0.10), "their" (0.08), and many lower, it samples only among those top five candidates.

Question: What is nucleus sampling (top-p) and why is it preferred over fixed-k?
Answer: Nucleus sampling dynamically selects the smallest set of tokens whose cumulative probability exceeds a threshold p (e.g., 0.9), then samples from that pool. Unlike fixed k, the pool size adapts to the model’s confidence: narrow when certain, broader when uncertain, for more consistency and diversity across varied contexts.
Example: With p = 0.85, if the top three tokens sum to 0.87, only those three are eligible, whereas if confidence is lower, additional tokens join until the 0.85 cutoff is reached.

Question: How does beam search differ and when is it advantageous?
Answer: Beam search is a deterministic decoding strategy that keeps b highest-scoring partial sequences (beams) at each step, expanding each by all possible next tokens and retaining only the top b full sequences by cumulative log-probability. It prioritizes globally coherent outputs, often improving overall sequence quality at the expense of diversity and computational cost.
Example: With beam width b = 3, the algorithm maintains three competing sentence hypotheses, e.g., "The cat sat", "The cat is", "The cat on" — and iteratively expands and ranks them to select the best final sentence.

Quiz
1. In top-k sampling, what happens when k is set very low?
A. The model samples from a very broad distribution
B. The model’s output becomes more random
C. The model’s output becomes more predictable (Correct)
D. The model ignores the highest-probability tokens

2. With nucleus sampling, the threshold p controls:
A. The maximum token length
B. The cumulative probability mass of eligible tokens (Correct)
C. The number of beams explored
D. The learning rate of the model

3. Beam search is typically chosen for tasks that require:
A. High diversity and randomness
B. Fast, single-step token sampling
C. Globally coherent and high-probability sequences (Correct)
D. Dynamic adjustment of sampling pool size

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Question: What is zero-shot prompting and when is it effective?
Answer: Zero-shot prompting involves providing only a task description or instruction with no exemplars, relying entirely on the model’s pre-trained knowledge to perform the task. It is effective for well-defined prompts where the model has seen similar contexts during pre-training. However, performance may degrade on new or highly specialized tasks without additional guidance.
Example: Asking "Translate ‘Good night’ to Spanish." without examples yields "Buenas noches" directly because the model understands the instruction.

Question: How does few-shot prompting improve task performance?
Answer: Few-shot prompting embeds a small number of input–output pairs in the prompt to demonstrate the desired format or style. These exemplars guide the model’s internal pattern recognition, boosting accuracy on tasks that are unfamiliar or ambiguous in zero-shot settings.
Example:

  Q: Summarize the following email in one sentence.   
  A: The project deadline has been moved up to Friday.   
  Q: Summarize the following email in one sentence.   
  A: We need to reschedule our meeting to next week.   
  Q: Summarize the following email in one sentence.   
  A:

The model completes with a concise summary of the new email.

Question: What is chain-of-thought prompting and why does it enhance reasoning?
Answer: Chain-of-thought prompting instructs the model to articulate its intermediate reasoning steps before providing the final answer. By making its latent reasoning explicit, the model can solve complex, multi-step problems more reliably than when asked for a direct answer.
Example:

  Question: If there are 4 red and 6 blue marbles, what is the  
  probability of drawing two red marbles without replacement?   
  Chain of thought: There are 10 marbles total; first draw probability  
  is 4/10. After one red is drawn, 9 marbles remain with 3 red; second  
  draw probability is 3/9. Multiply 4/10 × 3/9 = 12/90 = 2/15.   
  Answer: 2/15

Question: How does role prompting influence the tone and depth of responses?
Answer: Role prompting assigns the model a specific persona or professional identity, such as "You are a cybersecurity analyst". This frames its internal context and adapts its vocabulary, tone, and level of detail to that role. This gives outputs that better align with domain expectations and stakeholder needs.
Example: "You are a marketing strategist. Propose three social media campaign ideas for a new product launch."

Question: What are advanced prompt tactics beyond basic prompting styles?
Answer: Advanced tactics include prompt chaining, where the output of one prompt feeds into the next; format enforcement, specifying JSON or XML output to facilitate parsing; dynamic parameter injection, altering temperature or top-p mid-conversation; and adversarial prompt testing, probing the model’s limits to harden prompts against failure modes.
Example: Note: If you want to my shared resources, you can get them from my Kaggle profile at https://www.kaggle.com/inderpsingh

Quiz
1. Which prompting style uses only a task description without examples?
A. Chain-of-thought prompting
B. Few-shot prompting
C. Zero-shot prompting (Correct)
D. Role prompting

2. What is the key advantage of chain-of-thought prompting?
A. It reduces token usage
B. It enforces JSON output
C. It makes intermediate reasoning explicit, improving complex problem-solving (Correct)
D. It limits the model’s vocabulary

3. How does role prompting affect model responses?
A. It alters hyperparameters during sampling
B. It assigns a persona to tailor tone and depth (Correct)
C. It adds adversarial noise to inputs
D. It restricts output length

4. An example of an advanced prompt tactic is:
A. Masking 15% of tokens during training
B. Using prompt chaining where one prompt’s output informs the next (Correct)
C. Applying beam search during inference
D. Fine-tuning on a domain-specific corpus

Question: What role do model parameters and scale have in generative AI performance?
Answer: Model parameters (the learned weights and biases in an LLM) determine its capacity to encode linguistic patterns, factual knowledge, and reasoning heuristics. Scale, measured by the total number of parameters, directly impacts the model’s expressiveness: larger models can capture subtler dependencies and rare phenomena in language. However, as scale increases from millions to billions or trillions of parameters, training and inference costs grow nonlinearly, requiring more FLOPs (FLOating Point operations) and specialized hardware.
Example: Moving from a 1 billion-parameter model to a 100 billion-parameter model can yield dramatic improvements in text coherence and knowledge recall, but may demand 50× more compute during training.

Question: What are FLOPs and why are they important in LLM scaling?
Answer: FLOPs quantify the number of floating-point arithmetic operations a model performs during training or inference. Higher FLOPs indicate more intensive computation, inferring deeper networks, larger hidden dimensions, and broader attention mechanisms. Tracking FLOPs helps teams estimate GPU-hour requirements, energy costs, and latency trade-offs when scaling up or deploying in production.
Example: A training run requiring 10²³ FLOPs might take weeks on a cluster of top-tier accelerators, whereas a smaller model needing 10²¹ FLOPs could finish in days on more modest hardware.

Question: What are emergent abilities in large generative models, and why do they matter?
Answer: Emergent abilities are capabilities, such as multi-step reasoning, code synthesis, or translation, that appear suddenly once a model surpasses a critical parameter threshold. They are not linearly predictable from smaller-scale performance and often manifest only in very large models. Still, research suggests that some abilities can appear in moderately sized models as well.

Recognizing emergent phenomena guides investment decisions: practitioners may choose to scale to unlock new functionalities rather than invest solely in algorithmic tweaks.
Example: Code generation reliability often "turns on" in models above ~20 billion parameters, enabling use cases like automated unit-test creation, an ability absent in smaller models.

Quiz
1. Increasing scale in LLMs primarily boosts which aspect?
A. Training speed
B. Model expressiveness and capacity (Correct)
C. Dataset size
D. Number of GPUs required

2. What do FLOPs measure?
A. Data storage requirements
B. Number of floating-point operations (Correct)
C. Tokenization speed
D. Model accuracy

3. An emergent ability is :
A. A capability that degrades at larger scales
B. A feature that arises only after fine-tuning
C. A capability that appears abruptly once a scale threshold is crossed (Correct)
D. A performance metric for inference latency

Question: What are hallucinations and biases in generative LLMs, and why do they undermine reliability?
Answer: Hallucinations occur when an LLM fabricates plausible (reasonable) sounding but factually incorrect statements, because it predicts tokens based on learned distributions rather than verifiable sources. Biases are systematic distortions, such as gender or cultural stereotypes, embedded in model outputs due to skewed training data. Together, hallucinations and biases erode user trust and can lead to harmful or misleading results in professional settings.
Example: An LLM might confidently "remember" a non-existent court ruling (hallucination) or default to male pronouns when describing a doctor (bias), both of which can have real-world consequences.

Question: How do Retrieval-Augmented Generation (RAG) and grounding anchor outputs to reduce hallucinations?
Answer: RAG pipelines fetch relevant documents from a vector store or database at inference time, then prepend or interleave these context snippets with the prompt. The LLM generates responses by referencing actual text, dramatically cutting hallucination. Grounding further enforces factuality by requiring the model to quote or cite source identifiers. This creates a feedback loop: if retrieved context lacks answers, the model signals uncertainty rather than inventing details.
Example: In medical advice generation, RAG retrieves the latest clinical guidelines, and the LLM echoes sections verbatim, such that the recommendations align with current best practices.

Question: What practices enhance trustworthiness in LLM outputs?
Answer: Enhancing trustworthiness involves combining technical and procedural safeguards: implementing bias audits and adversarial testing to expose failure modes; enforcing uncertainty calibration so models preface low-confidence answers appropriately; and instituting human-in-the-loop review for high-stakes content, such as client-facing content. Additionally, logging all prompts and responses enables traceability and post-hoc verification.
Example: A financial reporting assistant flags any answer with confidence below 70% and routes it to an analyst for approval before distribution.

Quiz
1. What is a hallucination in the context of generative LLMs?
A. A deliberate policy violation
B. A fabricated statement presented as fact (Correct)
C. A syntax error in generated text
D. A missing token in the vocabulary

2. How does RAG mitigate hallucinations?
A. By fine-tuning on labeled datasets
B. By retrieving and incorporating real documents into the prompt (Correct)
C. By increasing the temperature parameter
D. By masking future tokens

3. Which practice most directly improves trustworthiness?
A. Using only decoder-only architectures
B. Logging all prompts and responses for review (Correct)
C. Disabling self-attention layers
D. Increasing beam width during generation

Question: What is multimodal generation and how do LLMs extend beyond text?
Answer: Multimodal generation enables LLM-based systems to process and produce content across different data types (such as image, audio, and code) by integrating specialized encoders or tokenizers for each modality and unifying their representations in a shared transformer backbone. For image generation, the model might take a textual prompt like "A sunrise over a mountain top" and output pixel data or descriptive captions. In code generation, natural-language instructions are converted into language and syntax tokens, enabling the LLM to produce runnable functions. For audio, spectrogram or waveform tokens represent sound patterns, allowing the model to synthesize speech or music from text.
Example: A single prompt such as "Create a Python function that parses JSON logs and returns error counts" creates syntactically correct code ready for deployment.

Question: What is domain specialization in generative AI? Why is domain specialization needed for specialized content?
Answer: Domain specialization tailors a foundation LLM to excel in a narrow field, such as medical diagnosis, legal analysis, or financial forecasting, by fine-tuning on domain-specific corpora. This process adjusts the model’s parameters so it embeds relevant terminology, conventions, and reasoning patterns, reducing hallucinations and improving output precision for niche tasks. Specialized LLMs can generate regulatory-compliant reports, clinical summaries, or investment briefs with expert-level fidelity.
Example: Fine-tuning on thousands of peer-reviewed medical articles produces an AI assistant that drafts patient discharge summaries using correct medical tems and standardized formats.

Question: How does fine-tuning for niche content differ from general-purpose training?
Answer: Fine-tuning for niche content continues training a pre-trained LLM on a curated, high quality dataset from the target domain, typically with task-specific prompts and labels. Unlike broad pre-training, which focuses on scale and diversity, niche fine-tuning focuses on depth and accuracy, using lower learning rates and fewer epochs to preserve general language abilities while embedding domain expertise. This ensures the model generates content that aligns with professional standards, best practices, and regulatory requirements.
Example: A legal LLM fine-tuned on annotated case law excels at drafting motions, whereas a general LLM might mistake legal terminology or introduce irrelevant precedents.

Quiz
1. Which capability defines multimodal generation in LLMs?
A. Generating text summaries only
B. Processing and generating across image, audio, and code modalities (Correct)
C. Training without any data
D. Classifying tokens into fixed categories

2. Domain specialization primarily involves:
A. Reducing the size of the vocabulary
B. Fine-tuning on specific corpora to embed domain knowledge (Correct)
C. Increasing the number of transformer layers
D. Masking tokens during inference

3. When fine-tuning for niche content, which practice is most important?
A. Using high learning rates for rapid convergence
B. Training on a broad, general-purpose dataset
C. Curating a high-quality, domain-specific dataset with task-aligned prompts (Correct)
D. Removing all pre-training parameters before training

Note: If you want my full Generative AI with Large Language Models document with several more topics, you can message me in LinkedIn.