This repository addresses the need for structured preparation for Large Language Model (LLM) interviews. It offers a comprehensive collection of over 115 questions and answers, covering fundamental LLM concepts, Transformer architecture, inference optimization, prompt engineering, fine-tuning, and pretraining. The target audience includes AI/ML engineers, researchers, and students preparing for roles in the rapidly evolving LLM space. Its primary benefit is providing a focused, in-depth study resource to help candidates master key topics and articulate their understanding effectively.

How It Works

This project functions as a curated knowledge base and study guide for LLM technical interviews. It systematically presents questions categorized by critical LLM lifecycle stages and architectural components. The questions are designed to assess a candidate's grasp of theoretical underpinnings, practical implementation challenges, and the trade-offs inherent in developing and deploying LLM-based systems.

Quick Start & Requirements

This repository is a collection of questions and answers and does not require installation or execution. It serves as a reference document.

Highlighted Details

• Extensive Question Set: Features over 115 questions covering LLM fundamentals, Transformer architecture, inference techniques (KV cache, quantization, decoding strategies), prompt engineering, fine-tuning (including PEFT methods like LoRA/QLoRA), and pretraining concepts.
• Related Ecosystem: Links to supplementary repositories for prompt engineering techniques, LLM engineer toolkits, and survey papers, fostering a broader learning environment.
• Practical Focus: Questions address real-world challenges such as distributed inference, deployment bottlenecks, and performance metrics.

Maintenance & Community

The repository is maintained by KalyanKS-NLP. Community engagement is encouraged through a request to star the repository. Specific details regarding active development, release cadence, or dedicated community channels (e.g., Discord, Slack) are not provided in the README snippet.

Licensing & Compatibility

The provided README snippet does not specify a software license. This omission prevents an assessment of its terms for use, modification, or distribution, particularly concerning commercial applications.

Limitations & Caveats

The README snippet contains only the interview questions; the answers are not directly included and must be accessed within the repository's files. A significant limitation is the absence of explicit licensing information, which is crucial for evaluating adoption suitability. Furthermore, details on project maintenance, community support, and contribution guidelines are minimal.