Advanced AI Interview Questions
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01. What is the Attention Mechanism in AI?
Attention is a mechanism that allows a neural network to focus on the most relevant parts of the input while processing information.
Key Points
- Assigns importance to different input elements
- Used in Transformer architectures
- Helps LLMs understand relationships between tokens
02. What is Self-Attention?
Self-Attention calculates relationships between tokens within the same sequence using Query, Key, and Value representations.
Example:
In the sentence "The animal didn't cross the road because it was tired," self-attention can help the model relate "it" to "animal."
03. What is Retrieval-Augmented Generation (RAG)?
RAG combines information retrieval with text generation. It retrieves relevant documents and supplies them as context to an AI model before generating an answer.
Key Points
- Connects LLMs with external knowledge
- Can reduce unsupported answers when retrieval is relevant
- Used in document chatbots and knowledge assistants
04. What is the Difference Between RAG and Fine-Tuning?
RAG supplies external information during inference, while fine-tuning updates a model's parameters using training examples.
Key Points
- RAG is useful for frequently changing knowledge
- Fine-tuning can adapt behavior and output formats
- Both approaches can be combined
05. What is an AI Agent?
An AI Agent is a system that uses a model to interpret a goal, decide on actions, and interact with tools or external systems to complete tasks.
Example Workflow
User Request → Planning → Tool Call → Result Analysis → Final Response
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UPDATEGADH | Technology Tutorials & Interview Questions
#AdvancedAI #GenerativeAI #LLM #RAG #AIAgents #AIInterviewQuestions #Python #UPDATEGADH
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01. What is the Attention Mechanism in AI?
Attention is a mechanism that allows a neural network to focus on the most relevant parts of the input while processing information.
Key Points
- Assigns importance to different input elements
- Used in Transformer architectures
- Helps LLMs understand relationships between tokens
02. What is Self-Attention?
Self-Attention calculates relationships between tokens within the same sequence using Query, Key, and Value representations.
Example:
In the sentence "The animal didn't cross the road because it was tired," self-attention can help the model relate "it" to "animal."
03. What is Retrieval-Augmented Generation (RAG)?
RAG combines information retrieval with text generation. It retrieves relevant documents and supplies them as context to an AI model before generating an answer.
Key Points
- Connects LLMs with external knowledge
- Can reduce unsupported answers when retrieval is relevant
- Used in document chatbots and knowledge assistants
04. What is the Difference Between RAG and Fine-Tuning?
RAG supplies external information during inference, while fine-tuning updates a model's parameters using training examples.
Key Points
- RAG is useful for frequently changing knowledge
- Fine-tuning can adapt behavior and output formats
- Both approaches can be combined
05. What is an AI Agent?
An AI Agent is a system that uses a model to interpret a goal, decide on actions, and interact with tools or external systems to complete tasks.
Example Workflow
User Request → Planning → Tool Call → Result Analysis → Final Response
━━━━━━━━━━━━━━━━━━━━━━
UPDATEGADH | Technology Tutorials & Interview Questions
#AdvancedAI #GenerativeAI #LLM #RAG #AIAgents #AIInterviewQuestions #Python #UPDATEGADH