<2-3 sentences summarising what the project addresses and solves, the target audience, and the benefit.> This repository serves as a comprehensive cheat sheet for AI Engineering interviews, targeting roles like AI Engineer, Gen AI Engineer, and LLMOps Engineer. It offers a structured collection of questions and answers across a broad spectrum of AI engineering domains, aiming to equip candidates with essential knowledge for technical and behavioral assessments.

How It Works

Organized by key AI engineering disciplines—LLM fundamentals, prompt engineering, RAG, AI agents, fine-tuning, vector databases, system design, LLMOps, and AI safety—this resource presents curated interview questions with detailed answers. It explains core concepts, architectures, and practical implementation challenges, facilitating targeted learning and review for interview preparation.

Quick Start & Requirements

As a knowledge repository, there are no software installation or execution requirements. The primary "requirement" is a foundational understanding of AI/ML concepts. Users can quickly start by navigating the detailed Table of Contents to focus on specific areas for interview preparation.

Highlighted Details

• Broad AI Engineering Scope: Covers LLM fundamentals (Transformers, attention, embeddings), prompt engineering (zero-shot, CoT, ReAct), RAG architectures, AI agents (ReAct, tool use), fine-tuning (PEFT, LoRA, QLoRA, RLHF), and vector databases.
• Practical LLM Challenges: Addresses context window limits, hallucination mitigation, prompt injection defense, KV cache management, and scaling issues.
• Core Concepts: Details on Transformer variants, attention mechanisms, quantization, Mixture of Experts (MoE), Flash Attention, and Grouped-Query Attention (GQA).
• System Design & Operations: Includes Q&A on AI system design, LLMOps, production deployment, monitoring, cost optimization, and evaluation metrics (BLEU, ROUGE, G-Eval).
• Emerging Areas: Features multi-modal AI, AI safety, ethics, and responsible AI practices.

Maintenance & Community

Maintained by Amit Shekhar of Outcome School, the repository is slated for continuous updates with new questions and answers. Outcome School has a social media presence (X/Twitter, LinkedIn, YouTube), but specific community channels like Discord or Slack are not indicated.

Licensing & Compatibility

Licensed under the Apache License, Version 2.0, this permissive license allows broad use, including integration into commercial products and closed-source projects, with standard attribution requirements.

Limitations & Caveats

This is a static knowledge base, not executable software, limiting its direct adoption or forking. Its value is purely educational. Coverage depth may vary, and it represents a curated selection of interview questions rather than an exhaustive guide.