Handcrafted PDFs from every AmanAI Lab video. 25 notes · 13 topics
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A concise interview preparation bundle covering AI Agents, Agentic AI, LLMs, RAG, Tool Calling, LangGraph, Multi-Agent Systems, Memory, Evaluation, and AI Agent System Design, along with practical and scenario-based interview questions and answers
Prepare for Generative AI interviews with this complete 5-PDF interview preparation pack. It covers the most important GenAI concepts, technical interview questions, LLMs, RAG, AI Agents, and practical coding/interview preparation. Designed for AI/ML engineers, GenAI developers, and professionals preparing for GenAI coding and technical interviews.
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A complete, from-scratch walkthrough of how an LLM decides which tool to call, how tool execution works under the hood, and how the result flows back into the conversation — with a full LangChain + Groq implementation explained line by line.
Stateful AI Agent with Memory
Building a Stateful AI Agent with LangGraph
Reason ® Act ® Observe ® Reason. This guide covers the concept first, then builds the full agent line by line, then tests it, then closes with the exact questions interviewers ask once your code works.
The code working is only half the interview. This guide walks through the theory first, then the full implementation, then the follow-up questions that come after — the same structure a real interview actually follows.
With Detailed Answers · Company Tags · Real Interview Tips
Production Agentic Systems | Real Scenarios
a handful of questions in this guide are direct, candidate-reported quotes from real FDE interviews (marked ‘Verified’). The rest are the concepts and round-types confirmed, by multiple independent 2026 sources, to be commonly tested — framed honestly as that, not as guaranteed verbatim questions.
Every question here is something you'd actually be asked to CODE, not just explain — the kind of applied, hands-on task real LLM-application coding rounds use. Each one includes working code plus a plain-language explanation of what it does and why it's built that way.
Without memory, an agent forgets everything the moment a conversation ends. This guide explains how agents actually remember, in plain language, then connects each idea to the interview questions built around it.
30 coding questions AI/ML Engineers are actually asked — arrays to graphs to LRU caches, explained with approach, code, and complexity
20 Statistics & Probability Interview Questions and Answers for AI Engineers — every ML model, every A/B test, every evaluation metric is built on these ideas. This guide builds intuition first with a concrete example, then the precise definition, so you can actually reason through a stats question, not just recall a formula.
SQL · NoSQL · Vector DB · Blob Storage · When to Use What for AI
Load Balancer · Caching (Redis, CDN, Semantic) · Cache Invalidation
Networking · DNS · CDN · Forward & Reverse Proxy · API Gateway
10 System Design Questions Commonly Discussed in AI Engineering Interviews
20 AI Agent Guardrails & Safety Interview Questions and Answers, covered layer by layer: what goes in, what comes out, what actions are allowed, when a human needs to step in, and how you'd test that it all holds up.
What happens after the model works in your notebook — deploying it safely, watching it stay healthy, and knowing what to do the moment something breaks.
Every one of the 30 questions below is explained in plain language first, then backed by the mechanism, a worked example, and — wherever a graph is involved — an actual diagram you can picture in your head.
Real-World Scenarios | Debugging | Production Challenges | 2026 Not Theory — The Questions They ACTUALLY Ask About Your Projects
HyDE · Self-RAG · CRAG · GraphRAG · Agentic RAG · Multi-Modal