Ai Engineering Interview Questions

---
name: ai-engineering-interview-questions
description: Comprehensive cheat sheet and study guide for AI Engineering interview questions covering LLMs, RAG, agents, fine-tuning, quantization, and more.
triggers:
  - help me prepare for an AI engineering interview
  - what are common LLM interview questions
  - explain RAG interview topics
  - AI agent interview preparation
  - fine-tuning and quantization interview questions
  - LLMOps interview questions and answers
  - prompt engineering interview prep
  - vector database interview questions
---

# AI Engineering Interview Questions Skill

> Skill by [ara.so](https://ara.so) — Daily 2026 Skills collection.

## What This Project Does

[ai-engineering-interview-questions](https://github.com/amitshekhariitbhu/ai-engineering-interview-questions) is a curated, community-maintained cheat sheet of AI Engineering interview questions and answers. It covers all major topics an AI/ML/LLM engineer needs for interviews at AI-focused companies, targeting roles such as:

- AI Engineer / Gen AI Engineer
- LLM Engineer / Agentic AI Engineer
- AI Solutions Architect
- MLOps / LLMOps Engineer
- Applied AI Engineer

Topics span: LLM Fundamentals, Prompt Engineering, RAG, AI Agents, Fine-Tuning, Vector Databases, AI System Design, LLMOps, Evaluation, AI Safety, Multi-Modal AI, and Infrastructure.

---

## Installation / Setup

This is a Markdown reference repository — no installation required. Clone or bookmark it for study.

```bash
# Clone the repo locally for offline study
git clone https://github.com/amitshekhariitbhu/ai-engineering-interview-questions.git
cd ai-engineering-interview-questions

# Browse the main README
cat README.md

# Or open in your editor
code README.md

Topic Coverage Map

Use this map to navigate interview prep by role focus:

Core Concept Summaries

LLM Fundamentals

Key Concepts:
- Transformer architecture: encoder-only, decoder-only, encoder-decoder
- Self-attention: Q (Query), K (Key), V (Value) matrices
- Multi-head attention vs Grouped-Query Attention (GQA)
- Tokenization: BPE, WordPiece, SentencePiece
- Positional encoding (absolute, learned, RoPE)
- KV Cache: speeds up autoregressive inference by caching past K/V
- Mixture of Experts (MoE): sparse routing to expert sub-networks
- Flash Attention: memory-efficient attention with IO-aware tiling
- Context window: maximum tokens the model can process at once
- Temperature, Top-k, Top-p: controls for text generation randomness