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Top Generative AI Interview Questions for Freshers and Experienced Developers
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45 Questions2 PagesEasy · Medium · HardPage 1 of 2
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1
What is Generative AI and how is it different from traditional machine learning?
easybasicsml
Answer
Generative AI creates new data similar to training data, unlike traditional ML which predicts labels.
Key concept: Data generation vs classification.
Example: GPT generating text vs classifier predicting sentiment.
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2
Explain how Large Language Models (LLMs) work at a high level.
mediumllmtransformer
Answer
LLMs use transformer architecture to predict next tokens based on context.
Key concept: Self-attention mechanism.
Example: Predicting next word in a sentence.
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3
What is a transformer architecture and why is it important?
mediumtransformerarchitecture
Answer
Transformers process sequences using self-attention instead of RNNs.
Key concept: Parallel processing and context awareness.
Example: BERT, GPT models.
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4
What is tokenization in NLP models?
easytokensnlp
Answer
Tokenization splits text into smaller units (tokens).
Key concept: Model processes tokens, not raw text.
Example: 'hello world' → ['hello', 'world'].
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5
Explain the concept of embeddings in Generative AI.
mediumembeddingsvector
Answer
Embeddings convert text into numerical vectors.
Key concept: Semantic similarity.
Example: Similar words have closer vectors.
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6
What is fine-tuning in Generative AI?
mediumfinetuningtraining
Answer
Fine-tuning adapts a pre-trained model to a specific task.
Key concept: Transfer learning.
Example: Fine-tuning GPT for legal text.
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7
What is prompt engineering and why is it important?
mediumpromptdesign
Answer
Designing inputs to guide model outputs.
Key concept: Input strongly influences output.
Example: Structured prompts improve accuracy.
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8
Explain temperature parameter in text generation.
easyparametersllm
Answer
Controls randomness of output.
Key concept: Higher = more creative, lower = deterministic.
Example: temperature 0.2 for factual answers.
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9
What is top-k and top-p sampling?
hardsamplinggeneration
Answer
Methods to control token selection.
Key concept: Limit candidate tokens.
Top-k picks k tokens; top-p uses probability threshold.
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10
What is hallucination in Generative AI?
mediumhallucinationllm
Answer
When model generates incorrect or fabricated information.
Key concept: Lack of grounding.
Example: Fake facts in answers.
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11
How can hallucinations be reduced?
hardhallucinationrag
Answer
Use RAG, better prompts, and validation.
Key concept: Ground responses with data.
Example: retrieve documents before answering.
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12
What is Retrieval-Augmented Generation (RAG)?
mediumragretrieval
Answer
Combines retrieval system with generation model.
Key concept: External knowledge integration.
Example: search + generate answer.
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13
Explain the role of vector databases in Generative AI.
mediumvector-dbembeddings
Answer
Stores embeddings for similarity search.
Key concept: Fast retrieval of relevant data.
Example: Pinecone, FAISS.
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14
What is few-shot prompting?
mediumpromptfew-shot
Answer
Providing examples in prompt.
Key concept: Guides model behavior.
Example: showing sample Q&A.
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15
What is zero-shot learning?
mediumzero-shotlearning
Answer
Model performs task without examples.
Key concept: Generalization ability.
Example: classify unseen labels.
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16
What is the difference between fine-tuning and prompt engineering?
mediumfinetuningprompt
Answer
Fine-tuning updates model weights; prompt engineering changes input.
Key concept: Training vs inference control.
Prompt is cheaper and faster.
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17
How does self-attention work in transformers?
hardattentiontransformer
Answer
Each token attends to others in sequence.
Key concept: Context-aware representation.
Example: word meaning depends on sentence.
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18
What is positional encoding?
hardtransformerencoding
Answer
Adds position info to tokens.
Key concept: Transformers lack sequence order.
Example: sine/cosine encoding.
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19
What are diffusion models?
harddiffusionmodels
Answer
Generate data by reversing noise process.
Key concept: Stepwise denoising.
Used in image generation.
