LLM Fundamentals Explained: Top Generative AI Interview Questions & Expert Answers
Q1. What is a Large Language Model (LLM)?
A:
“A Large Language Model is a deep-learning model trained on massive text datasets to understand and generate human-like language. It uses a transformer architecture with self-attention, enabling it to capture long-range dependencies and process text efficiently in parallel.”
Q2. How does a transformer model work?
A:
“Transformers rely on self-attention, which allows the model to weigh the importance of each word relative to others in a sentence. This eliminates the sequential processing limitations of RNNs and allows transformers to scale efficiently across huge datasets.”
Q3. What is the difference between pre-training and fine-tuning?
A:
“Pre-training teaches the model general language patterns using large, diverse datasets. Fine-tuning adapts the model to a specific task or domain using a smaller, task-specific dataset. Fine-tuning helps the model specialize while leveraging the broad knowledge from pre-training.”
Q4. What are embeddings in LLMs?
A:
“Embeddings are numerical vector representations of words, sentences, or documents. They capture semantic meaning, allowing the model to compare similarity and understand context.”
LLM Fundamentals — Advanced Q&A
1. Explain the difference between tokenization, embeddings, and attention.
Answer:
Tokenization breaks text into units; embeddings convert these tokens into numerical vectors representing semantic meaning; attention determines how strongly each token relates to others within a sequence.
2. Why are transformers better than RNNs for language modeling?
Answer:
Transformers process text in parallel using self-attention, eliminating sequential limitations in RNNs, leading to better scalability and contextual understanding.
3. What is positional encoding and why do LLMs need it?
Answer:
Since transformers do not naturally understand sequence order, positional encoding injects numerical patterns that represent token positions.
4. What limits the context window of an LLM?
Answer:
Memory and computation scale quadratically with sequence length due to attention operations.
5. How do quantization and pruning improve LLM efficiency?
Answer:
Quantization reduces numerical precision; pruning removes redundant weights—both shrink model size and accelerate inference.
Large Language Model Interview Questions,
Transformer Model Explained,
LLM Fundamentals Guide,
Generative AI Interview Preparation,
AI Engineer Interview Questions,
Tokenization vs Embeddings,
Pre-Training and Fine-Tuning Explained,
Self-Attention Mechanism,
Positional Encoding in Transformers,
LLM Optimization Techniques.
Learn key LLM fundamentals, transformer model concepts, embeddings, attention mechanisms, and advanced Generative AI interview questions with expert answers. Perfect for AI, ML, and NLP job preparation.
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