Top Generative AI interview questions on LLMs, diffusion, prompts, RAG, agents, evaluation, safety, and app architecture.
Generative AI is a branch of AI that creates new content such as text, images, audio, video, code, designs, or structured data. It learns patterns from training data and generates outputs that resemble or extend those patterns.
Traditional AI often classifies, predicts, ranks, or detects patterns. Generative AI produces new content. For example, a traditional model may classify an email as spam, while a generative model can draft a new email response.
Common examples include chatbots, text summarizers, code assistants, image generators, video generators, music generators, synthetic data tools, document drafting systems, and AI design assistants.
A foundation model is a large model trained on broad data that can be adapted to many tasks. Examples include large language models, vision-language models, and diffusion models. They are usually reused through prompting, fine-tuning, or retrieval augmentation.
A Large Language Model, or LLM, is a generative model trained to understand and produce text by predicting tokens. It can answer questions, summarize, translate, write code, classify text, and follow instructions.
A diffusion model is a generative model that learns to create data by reversing a noise process. It is widely used for image generation because it can start from noise and gradually denoise it into a realistic image guided by a prompt.
A Generative Adversarial Network, or GAN, uses two neural networks: a generator that creates outputs and a discriminator that tries to detect fake outputs. They train against each other, improving the generated quality over time.
A Variational Autoencoder, or VAE, is a generative model that learns a compressed latent representation of data and samples from that representation to generate new examples. VAEs are useful for structured generation and representation learning.
A transformer is a neural network architecture based on attention mechanisms. It is the backbone of many modern LLMs because it can model relationships between tokens and process long sequences efficiently.
Tokenization splits text into smaller units called tokens, such as words, subwords, or characters. LLMs process tokens rather than raw text, so tokenization affects context length, cost, and how the model understands input.
A context window is the maximum amount of text or tokens a model can consider at one time. If the prompt, retrieved documents, and conversation history exceed this limit, some information must be shortened, summarized, or removed.
Prompt engineering is the practice of writing and structuring instructions, context, examples, and constraints so a generative model produces useful output. Good prompts define the task, format, tone, audience, and boundaries clearly.
Zero-shot prompting asks a model to perform a task without examples. It relies on the model generalizing from training and instructions. It works well for common tasks but may be less reliable for specialized formats or domain-specific work.
Few-shot prompting includes a small number of examples in the prompt to demonstrate the expected behavior. It is useful when the task format, style, or decision boundary is hard to explain with instructions alone.
Chain-of-thought prompting encourages step-by-step reasoning. In production systems, it is often better to ask for concise reasoning summaries or structured intermediate checks rather than exposing long hidden reasoning to users.
Temperature controls randomness in generation. Lower temperature makes outputs more focused and deterministic. Higher temperature makes outputs more diverse and creative but can increase inconsistency or mistakes.
Top-p, or nucleus sampling, limits generation to the smallest set of likely tokens whose cumulative probability reaches a threshold. It helps balance creativity and coherence by avoiding very unlikely tokens.
Hallucination happens when a generative model produces content that sounds plausible but is false, unsupported, or fabricated. It is common when the model lacks grounding, context, or verification.
Hallucinations can be reduced with retrieval-augmented generation, citations, strict prompts, structured outputs, validation checks, lower temperature, domain-specific grounding, human review, and refusing to answer when evidence is missing.
Retrieval-Augmented Generation, or RAG, combines search with generation. The system retrieves relevant documents and gives them to the model as context so the answer is grounded in current or private knowledge.
RAG is useful because it can provide fresh, domain-specific, or private information without retraining the model. It also improves traceability when answers include citations or references to retrieved sources.
An embedding is a numeric vector that represents the meaning of text, images, or other data. Similar items have vectors close to each other, which makes embeddings useful for semantic search, recommendations, clustering, and RAG.
A vector database stores embeddings and supports similarity search. In Generative AI, it is often used to retrieve relevant documents or examples before sending context to an LLM.
An AI agent is a system that uses a model to decide actions, call tools, observe results, and continue working toward a goal. Agents can be useful for multi-step workflows but require strong limits, logging, and safety controls.
Tool calling allows a model to request external functions, APIs, databases, or services. Instead of only generating text, the model can ask the application to perform an action such as searching data, booking a meeting, or calculating a value.
Function calling is a structured form of tool calling where the model returns a function name and arguments. The application executes the function and can pass the result back to the model.
Structured output means the model returns data in a required format such as JSON. It is important for automation because downstream systems need predictable fields, types, and validation.
Evaluate it using task success, factual accuracy, relevance, completeness, safety, latency, cost, user satisfaction, and failure rate. For many applications, combine automated checks with human review and real user feedback.
An evaluation dataset is a collection of prompts, expected answers, labels, or grading criteria used to test model behavior. It should include normal cases, edge cases, unsafe requests, domain-specific examples, and regression tests.
Human evaluation uses reviewers to judge outputs for quality, accuracy, helpfulness, tone, safety, and domain correctness. It is important because automatic metrics often miss subtle errors or user experience issues.
Prompt injection is an attack where a user or retrieved content tries to override system instructions or manipulate the model. It can cause data leakage, unsafe actions, or incorrect tool usage.
Mitigation includes separating trusted and untrusted content, limiting tool permissions, validating tool arguments, using allowlists, filtering retrieved content, monitoring suspicious prompts, and never relying on prompts alone for security.
A jailbreak is an attempt to bypass safety instructions and make the model produce disallowed or harmful output. Defenses include model safety tuning, policy filters, prompt hardening, monitoring, and output moderation.
Content moderation checks generated or user-provided content for unsafe, harmful, illegal, private, or policy-violating material. It can be applied before generation, after generation, or both.
Safety guardrails are controls that constrain model behavior. Examples include input filters, output filters, refusal rules, tool restrictions, human review, audit logs, rate limits, and access control.
Fine-tuning adapts a pretrained model using additional examples for a specific task, tone, format, or domain. It is useful when prompting is not enough, but it requires high-quality training data and evaluation.
Fine-tuning changes model behavior by training on examples. RAG supplies external knowledge at request time. Fine-tuning is better for style or task behavior; RAG is better for current, private, or frequently changing knowledge.
Synthetic data is artificially generated data used for training, testing, simulation, or augmentation. It can help when real data is limited, but it must be checked for realism, bias, privacy risk, and overfitting to generated patterns.
Multimodal Generative AI can process or generate more than one type of data, such as text, images, audio, video, or documents. Examples include image captioning, visual question answering, and text-to-image generation.
Text-to-image generation creates images from natural language prompts. It is commonly powered by diffusion models and is used for design, marketing, concept art, product mockups, and creative workflows.
Code generation uses a generative model to write, explain, refactor, or test code. It can improve developer productivity, but generated code must be reviewed for correctness, security, maintainability, and licensing risk.
Latency is the time between a user request and model response. It is affected by model size, context length, network time, retrieval, tool calls, output length, and streaming. Poor latency can make GenAI products feel unusable.
Cost can be controlled by choosing smaller models when possible, shortening prompts, caching responses, limiting output length, batching requests, using RAG selectively, monitoring token usage, and routing simple tasks to cheaper models.
Response streaming sends generated output to the user as it is produced instead of waiting for the full answer. It improves perceived speed and is useful for chat, writing assistants, and long responses.
Model routing sends different requests to different models based on complexity, cost, latency, language, safety, or domain. A simple request may use a smaller model, while a complex reasoning task may use a stronger model.
Grounding means connecting generated output to reliable sources, facts, tools, or retrieved documents. Grounding reduces unsupported answers and is especially important for enterprise, legal, healthcare, and support applications.
Attribution means showing where generated information came from, such as citations, source links, document names, or retrieved passages. It helps users verify answers and builds trust.
Common risks include hallucination, privacy leakage, prompt injection, biased outputs, unsafe content, copyright issues, overreliance by users, high cost, poor evaluation, and insecure tool usage.
A safe deployment includes clear use-case boundaries, input validation, output moderation, RAG or grounding when needed, logging, monitoring, rate limits, user feedback, human review for high-risk decisions, and rollback plans.
A good Generative AI product solves a real user problem, produces reliable outputs, handles uncertainty honestly, is fast enough, controls cost, protects data, includes safety guardrails, and measures success with user and business metrics.
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