LLM Interview Questions and Answers Book

This book contains 100+ LLM interview questions with answers and detailed explanations covering

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 1️⃣ Transformers

 2️⃣ LLM Architectures

 3️⃣ LLM Pretraining 

 4️⃣ LLM Inference

 5️⃣ Prompting Techniques

 6️⃣  LLM Fine-Tuning

This book will be highly useful to Data Scientists, AI/ML Engineers, Software Engineers preparing for AI/ML interviews.

Some of the interesting questions from this book are

[1] How would you handle LLM fine-tuning on consumer hardware with limited GPU memory?

[2] After tokenization, how are tokens converted into embeddings in the Transformer model?

 [3] How do LLMs handle out-of-vocabulary (OOV) words?

[4] Explain the trade-offs in using a large vocabulary in LLMs.

[5] Explain how parameter efficient fine-tuning addresses the limitations of full fine-tuning.

[6] Explain the purpose of ReAct prompting in AI Agents.

[7] Describe a strategy for reducing hallucinations via prompt design.

[8] What are the different approaches for choosing examples for few-shot prompting?

[9] Explain the reason behind the effectiveness of Chain-of-Thought (CoT) prompting.

[10] What are the possible options for accelerating LLM inference? 

etc.