Interview Preparation
Generative AI Interview Questions and Answers for Freshers — 2026
These are the most commonly asked Generative AI interview questions for freshers in 2026 — compiled by Vtricks faculty based on real interview feedback from students placed at companies like Google, Microsoft, Accenture, TCS, AI product startups in Bangalore.
There are currently 1,800+ active Generative AI job openings in Bangalore. Freshers can expect ₹6–10 LPA at companies across Bangalore's tech corridor — Whitefield, Electronic City, Koramangala, and the CBD. Preparation matters: candidates who practise these questions consistently perform significantly better in technical rounds.
Interview Tips from Vtricks Faculty
- Always explain your reasoning process — interviewers want to see how you think, not just the final answer.
- Use real examples from projects you have worked on when answering scenario-based questions.
- If you don't know the answer, say so honestly and describe how you would find the answer — this is better than guessing.
- For Bangalore companies specifically: be ready to answer follow-up questions — they often go 2-3 levels deep on any concept.
- Always ask clarifying questions before answering complex scenario-based questions — this demonstrates professional problem-solving approach.
Easy — basic concept check
Medium — applied knowledge
All 20 Questions
Generative AI Interview Questions — Freshers
Q1. What is Generative AI and how is it different from traditional AI?
Conceptual
Easy
ANSWER
Generative AI is a type of artificial intelligence that can create new content — text, images, audio, video, and code — based on patterns learned from training data. Traditional (discriminative) AI classifies or predicts from existing data (spam detection, image classification). Generative AI creates new data that resembles the training data. Examples: ChatGPT and Claude generate text, DALL-E and Midjourney generate images, GitHub Copilot generates code, ElevenLabs generates voice. The breakthrough came from transformer architecture (2017) and large-scale compute that enabled Large Language Models (LLMs) to learn complex patterns from billions of text examples.
Q2. What is a Large Language Model (LLM)?
Conceptual
Easy
ANSWER
A Large Language Model is a deep learning model trained on vast amounts of text data to understand and generate human language. 'Large' refers to billions of parameters (GPT-4 has over 1 trillion parameters). LLMs are trained to predict the next token (word/subword) in a sequence — this simple objective, at massive scale, produces models that can answer questions, write code, summarise documents, and translate languages. Popular LLMs: GPT-4o (OpenAI), Claude 3 (Anthropic), Gemini (Google), Llama 3 (Meta — open source), Mistral. Each has different strengths, context windows, pricing, and use cases.
Q3. What is prompt engineering and why is it important?
Technical
Easy
ANSWER
Prompt engineering is the practice of designing and optimising input prompts to guide LLMs to produce desired outputs. It is important because the same LLM produces very different quality results depending on how the question is phrased. Key techniques: Zero-shot — ask directly without examples. Few-shot — provide 2–5 examples of desired input-output pairs before the actual question. Chain-of-thought (CoT) — ask the model to think step by step ('Let's think step by step') — dramatically improves complex reasoning. System prompt — set the model's role and behaviour. Output formatting — ask for JSON, bullet points, or specific structure. Good prompt engineering can often outperform fine-tuning for many use cases.
Q4. What is the difference between zero-shot, one-shot, and few-shot prompting?
Technical
Easy
ANSWER
Zero-shot prompting — asks the LLM to perform a task without providing any examples. Relies entirely on the model's pre-trained knowledge. Example: 'Classify this review as positive or negative: I loved this product!' One-shot prompting — provides exactly one example before the actual task. Helps the model understand the desired format. Few-shot prompting — provides 2–5 examples before the task. More examples generally improve performance for complex tasks. Research shows few-shot prompting can match or exceed fine-tuning for many tasks with the right examples. Choose based on task complexity and how many high-quality examples you have available.
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Q5. What is RAG (Retrieval Augmented Generation)?
Technical
Easy
ANSWER
RAG is a technique that enhances LLM responses by first retrieving relevant information from an external knowledge base and then using that information as context for generation. Why it matters: LLMs have a knowledge cutoff date and cannot access private data. RAG solves both problems. How it works: User asks a question → system converts question to an embedding vector → searches a vector database for similar chunks → retrieves top-k relevant chunks → includes them in the prompt context → LLM generates answer grounded in retrieved information. Use cases: customer support bots that reference product documentation, enterprise search over internal documents, Q&A over PDFs and knowledge bases.
Q6. What is the OpenAI API and how do you use it in Python?
Technical
Easy
ANSWER
The OpenAI API provides programmatic access to GPT-4, DALL-E, Whisper, and other OpenAI models. Basic Python usage: from openai import OpenAI; client = OpenAI(api_key='your-key'). Chat completion: response = client.chat.completions.create(model='gpt-4o', messages=[{'role':'system','content':'You are a helpful assistant.'}, {'role':'user','content':'What is RAG?'}]); print(response.choices[0].message.content). Key parameters: model (gpt-4o, gpt-3.5-turbo), messages (conversation history), temperature (0=deterministic, 1=creative), max_tokens (limit output length), stream=True for streaming responses. Always store API keys in environment variables, never in code.
Q7. What are embeddings and why are they central to Generative AI applications?
Technical
Easy
ANSWER
Embeddings are dense numerical vector representations of text (or images, audio) that capture semantic meaning — similar meaning = similar vectors in vector space. 'king' - 'man' + 'woman' ≈ 'queen' in vector space. Why they matter: they allow computers to measure semantic similarity. Applications: Semantic search — find documents similar in meaning, not just keyword matches. RAG — retrieve contextually relevant chunks for LLM context. Recommendation systems — find similar products, articles, users. Anomaly detection — find text that differs significantly from normal. Create embeddings with OpenAI's text-embedding-3-small model or open-source models from HuggingFace. Store and search in vector databases: Pinecone, Weaviate, Chroma, FAISS.
Q8. What is LangChain and what problems does it solve?
Technical
Easy
ANSWER
LangChain is a Python framework for building applications powered by LLMs. It solves the problem of connecting LLMs to external data sources, tools, and other LLMs in a composable way. Key components: Prompts — prompt templates with variables. Models — wrappers for various LLMs (OpenAI, Anthropic, HuggingFace). Chains — sequences of LLM calls and other operations. Agents — LLMs that decide which tools to use to complete a task. Memory — persist conversation history across interactions. Retrievers — fetch relevant documents for RAG. Tools — functions agents can call (web search, calculator, database queries). LangChain is popular for: RAG chatbots, autonomous agents, document analysis, structured data extraction from text.
Q9. What is the difference between fine-tuning and RAG?
Technical
Medium
ANSWER
Fine-tuning trains an existing pre-trained LLM on a specific dataset to update the model weights — making it better at a particular task or style. It requires: labelled training data (expensive to create), compute for training (GPU hours), periodic retraining as data changes. Best for: changing model style and tone, teaching consistent output format, learning domain-specific language. RAG retrieves external information at inference time and adds it to the prompt context — the model weights are not changed. No training required, knowledge is easily updated (just update the vector database), transparent (you can see what was retrieved). Best for: providing up-to-date information, accessing private documents, reducing hallucinations on factual questions.
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Q10. What is a vector database and how does it work?
Technical
Medium
ANSWER
A vector database stores high-dimensional vector embeddings and enables fast similarity search — finding the most similar vectors to a query vector. Traditional databases store and search structured data with exact matches. Vector databases enable semantic search — finding conceptually similar content. How it works: text chunks are embedded into vectors and stored. At query time, the query is embedded using the same model, then the database searches for the nearest vectors using algorithms like HNSW (Hierarchical Navigable Small World) or IVF (Inverted File Index) — approximate nearest neighbour search is much faster than exact search for high-dimensional vectors. Popular options: Pinecone (managed), Weaviate (open source), Chroma (lightweight, local), Qdrant, FAISS (Facebook's library for large-scale similarity search).
Q11. What is hallucination in LLMs and how do you reduce it?
Conceptual
Easy
ANSWER
Hallucination in LLMs refers to the model confidently generating information that is factually incorrect, made up, or not supported by the source documents. LLMs are trained to produce fluent text — they prioritise sounding correct over being correct. Techniques to reduce hallucination: RAG — ground responses in retrieved source documents, ask the model to only answer from provided context. Temperature — lower temperature (0–0.3) for factual tasks, higher for creative. Explicit instructions — 'Only answer based on the provided context. If the information is not in the context, say so.' Chain-of-thought — ask model to reason step by step before answering. Self-consistency — generate multiple responses and take the majority answer. Verification step — add a separate LLM call to fact-check the response.
Q12. What is the context window in an LLM and why does it matter?
Conceptual
Easy
ANSWER
The context window is the maximum amount of text (measured in tokens) an LLM can process in a single request — both input prompt and output response combined. GPT-4 has a 128K token context window. Claude 3.5 has 200K tokens. Approximately 1 token ≈ 0.75 words or 4 characters. Why it matters: it limits how much document text you can include in a single prompt, how long conversation history can be maintained, and the size of code or data you can analyse at once. For RAG, the context window determines how many retrieved chunks you can include. Larger context windows reduce the need for complex chunking strategies but increase API costs (you pay per token).
Q13. What are AI agents and how do they differ from simple LLM calls?
Conceptual
Medium
ANSWER
An AI agent is a system where an LLM autonomously decides what actions to take to complete a goal — it can call tools, make multiple decisions, and iterate until the task is done. A simple LLM call is a single request-response — you provide all the context, the model answers once. An agent: receives a goal, decides which tools to use (web search, code execution, database query, calculator), executes the tool, observes the result, decides the next action, and repeats until the goal is achieved. Frameworks for building agents: LangChain agents, LlamaIndex agents, AutoGen (Microsoft), CrewAI (multi-agent), OpenAI Assistants API. Challenges: agents can get stuck in loops, make expensive mistakes in production, and are harder to debug than simple chains.
Q14. What is the system prompt and how does it affect LLM behaviour?
Technical
Easy
ANSWER
The system prompt (also called system message) is a special instruction given to the LLM at the beginning of a conversation that sets its persona, behaviour, and constraints. It is invisible to the end user but shapes all subsequent responses. Examples: 'You are a customer support agent for Vtricks. Only answer questions about our Data Analytics courses. Be concise and helpful. If asked about pricing, always mention the free demo class.' System prompts are used to: set the model's role and personality, restrict topics the model will discuss, define output format, provide background context, set language or tone requirements. The system prompt typically has higher precedence than user messages — it is a primary tool for customising LLM applications.
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Q15. What is Hugging Face and why is it important in the Gen AI ecosystem?
Conceptual
Easy
ANSWER
Hugging Face is a platform and open-source library that has become the central hub for sharing and using machine learning models, datasets, and demos. It is important because: it hosts thousands of open-source pre-trained models (Llama, Mistral, Stable Diffusion) you can download and use for free. The transformers library provides a unified API to use any of these models with 2–3 lines of code. Datasets library for accessing training and benchmark datasets. Spaces for hosting and sharing AI demos. The Hub (huggingface.co) is like GitHub for AI models. For GenAI development: use Hugging Face when you need open-source models (no API costs, data privacy), want to fine-tune models, or need specialised models for specific domains.
Q16. Explain the basic architecture of a RAG chatbot.
Technical
Medium
ANSWER
A RAG chatbot has two phases: Indexing (offline): Load documents (PDFs, websites, databases). Split into chunks (500–1000 tokens with overlap). Generate embeddings for each chunk using embedding model. Store chunks and embeddings in vector database. Query (online): User sends message. Convert user message to embedding. Search vector database for top-k most similar chunks. Retrieve those chunks (context). Construct prompt: system message + retrieved context + conversation history + user question. Send to LLM. Stream response back to user. Optional: add memory to maintain conversation context. Implementation: Python + LangChain + OpenAI embeddings + Chroma vector store + GPT-4 is the most common tech stack for building RAG chatbots.
Q17. What is tokenisation in LLMs?
Technical
Easy
ANSWER
Tokenisation is the process of breaking text into smaller units (tokens) that the LLM processes. Tokens are not exactly words — they are subword units. Common words are single tokens ('hello' = 1 token). Rare words are split into multiple tokens ('Bangalore' = 2 tokens: 'Bang', 'alore'). Numbers, punctuation, and whitespace are also tokens. Why it matters: LLMs are billed by tokens (input + output). Context window limits are in tokens, not words. Tokenisation affects how models handle rare words and languages. GPT models use Byte Pair Encoding (BPE) tokenisation. Approximately: 1 token ≈ 4 characters or 0.75 words in English. Other languages use more tokens per word — Hindi or Telugu text uses significantly more tokens than the same content in English.
Q18. What is temperature in LLM generation and when do you adjust it?
Technical
Easy
ANSWER
Temperature is a parameter (0–2, typically 0–1) that controls the randomness of LLM output. Low temperature (0–0.3) — model chooses high-probability tokens, output is more deterministic, focused, consistent. Best for: factual Q&A, code generation, data extraction, classification. High temperature (0.7–1.0) — model considers lower-probability tokens, output is more creative, varied, surprising. Best for: creative writing, brainstorming, generating diverse options. Temperature 0 — nearly deterministic (same prompt → same output every time). Temperature 1 — standard creative setting. Temperature 2 — very random, often incoherent. For most business applications, use 0 for factual tasks and 0.5–0.7 for creative tasks. Never set above 1 for production systems.
Q19. What skills do you need to become a Generative AI engineer?
Conceptual
Easy
ANSWER
Core skills for a Generative AI engineer: Python — proficiency in Python for API integration, data processing, and application development. LLM APIs — OpenAI, Anthropic Claude, Google Gemini API usage and prompt engineering. Vector databases — Pinecone, Chroma, Weaviate for embedding storage and retrieval. LangChain or LlamaIndex — frameworks for building LLM applications and RAG systems. Embedding models — understanding how to generate and use embeddings for semantic search. Basic ML concepts — understanding of transformers, fine-tuning, and evaluation metrics. Prompt engineering — ability to craft effective prompts for different tasks. Web development basics — building simple interfaces for AI applications using FastAPI or Streamlit. These skills, combined with domain knowledge in a specific industry, make a highly employable Gen AI engineer in 2026.
Q20. What are the ethical considerations in Generative AI?
Conceptual
Easy
ANSWER
Key ethical considerations: Hallucination and reliability — LLMs can confidently state false information, which is dangerous in medical, legal, or financial contexts. Bias — LLMs inherit biases from training data and can generate biased content against certain groups. Privacy — models trained on internet data may memorise and reproduce private information. Copyright — models trained on copyrighted content may reproduce protected material. Misuse — deepfakes, disinformation, phishing email generation, academic dishonesty. Environmental impact — training and running large models consumes significant energy. Job displacement — automation of knowledge work tasks. Responsible AI practices: transparency about AI use, human oversight for high-stakes decisions, content filtering, privacy-preserving techniques, regular bias audits, and clear attribution.
Company Insights
What Generative AI Companies in Bangalore Actually Ask
Based on interview feedback from Vtricks students placed at Bangalore companies in 2026:
Round 1 — Written/Online Test
Most Bangalore companies start with a written or online test covering generative ai fundamentals, multiple choice questions on Python and LangChain, and basic problem-solving questions. Duration: 30–60 minutes. Companies like Google and Microsoft use platforms like HackerRank or their own internal assessments.
Round 2 — Technical Interview (Most Important)
This is where most candidates are filtered. Expect: direct questions from this list, hands-on tasks (write a SQL query, debug a piece of code, explain a dashboard you built), and scenario-based questions where you walk through how you would solve a real problem. Be prepared to share your screen and code live.
Round 3 — Managerial / HR Round
Focuses on: why you chose generative ai as a career, how you handle ambiguous requirements, a project you are proud of (have this ready in detail — situation, what you did, result), and salary expectations. Research the company's tech stack and recent news before this round.
Tools You Must Be Able to Demonstrate
- Python — be ready to use this live in an interview
- LangChain — be ready to use this live in an interview
- OpenAI API — be ready to use this live in an interview
- HuggingFace — be ready to use this live in an interview
- RAG — be ready to use this live in an interview
- Prompt Engineering — be ready to use this live in an interview
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