AI Engineer • Coding • medium

Write a Python class to manage conversation history for a multi-turn chatbot.

#Chatbot #Memory

Practice

AI Engineer • Technical • hard

What is RAG (Retrieval-Augmented Generation)? When would you use it over fine-tuning?

#RAG #Fine-Tuning

Practice

AI Engineer • Technical • hard

What is hallucination in LLMs? How do you detect and mitigate it?

#Hallucination #Safety

Practice

AI Engineer • Technical • medium

Explain the difference between autoregressive and masked language modeling.

#Autoregressive #Masked LM

Practice

AI Engineer • Technical • hard

What is a mixture of experts (MoE) architecture? How does it scale?

#MoE #Scaling

Practice

AI Engineer • Technical • hard

Explain function calling / tool use in LLMs. How do you implement it?

#Function Calling #Tool Use

Practice

AI Engineer • Technical • medium

Explain structured output generation from LLMs (JSON mode, Instructor library).

#Structured Output #JSON

Practice

AI Engineer • Technical • hard

Explain the difference between GPT, BERT, and T5 architectures.

#GPT #BERT #T5

Practice

AI Engineer • Technical • medium

What is prompt engineering? What are few-shot, zero-shot, and chain-of-thought prompting?

#Prompt Engineering #Few-Shot

Practice

AI Engineer • Technical • hard

Explain how RLHF (Reinforcement Learning from Human Feedback) improves LLMs.

#RLHF #Alignment

Practice

AI Engineer • Technical • medium

Explain the difference between fine-tuning and in-context learning.

#Fine-Tuning #ICL

Practice

AI Engineer • Technical • medium

What is token context window? How do you handle documents longer than the context limit?

#Context Window #Chunking

Practice

ML Engineer • System Design • hard

Design a training and serving architecture for a large language model at scale.

#Infrastructure #Scale

Practice

ML Engineer • Technical • hard

What is RAG (Retrieval-Augmented Generation)? Describe its architecture.

#RAG #Vector Search

Practice

ML Engineer • Technical • hard

How would you evaluate an LLM for a production use case?

#Evaluation #Benchmarking

Practice

ML Engineer • Technical • hard

What is LoRA (Low-Rank Adaptation)? How does it reduce fine-tuning costs?

#LoRA #Fine-Tuning

Practice

ML Engineer • Technical • hard

Explain the RLHF (Reinforcement Learning from Human Feedback) training approach.

#RLHF #Fine-Tuning

Practice

Meta

The Interview Loop

Recruiter Screen (30 min)

Technical Loop (3-4 Rounds)

Interview Question Bank

Write a Python class to manage conversation history for a multi-turn chatbot.

What is RAG (Retrieval-Augmented Generation)? When would you use it over fine-tuning?

What is hallucination in LLMs? How do you detect and mitigate it?

Explain the difference between autoregressive and masked language modeling.

What is a mixture of experts (MoE) architecture? How does it scale?

Explain function calling / tool use in LLMs. How do you implement it?

Explain structured output generation from LLMs (JSON mode, Instructor library).

Explain the difference between GPT, BERT, and T5 architectures.

What is prompt engineering? What are few-shot, zero-shot, and chain-of-thought prompting?

Explain how RLHF (Reinforcement Learning from Human Feedback) improves LLMs.

Explain the difference between fine-tuning and in-context learning.

What is token context window? How do you handle documents longer than the context limit?

Design a training and serving architecture for a large language model at scale.

What is RAG (Retrieval-Augmented Generation)? Describe its architecture.

How would you evaluate an LLM for a production use case?

What is LoRA (Low-Rank Adaptation)? How does it reduce fine-tuning costs?

Explain the RLHF (Reinforcement Learning from Human Feedback) training approach.

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