Handcrafted PDFs from every AmanAI Lab video. 46 notes · 18 topics
YouTube Member
Enter monthly code → all notes free
Non-Member
Pay once · instant download · no login
A complete lecture-style coding series covering End-to-End LLMs and Agentic AI, designed specifically for Agentic AI coding interviews
Get 14 interview-focused PDFs covering SQL, Machine Learning, Deep Learning, LLMs, Generative AI, LangChain, LangGraph, AI Agents, Agentic AI, RAG & Production AI — everything you need to prepare for modern AI Engineer & ML Engineer interviews in one complete bundle.
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.
46 notes
LangChain Foundations: Chat Models, Messages, Runnables and Your First App Beginner to advanced · Theory, code and examples · Exercises and interview questions
L2/L3 GenAI & Agentic AI Scenario Round
11 most-asked situational questions for Forward Deployed Engineer / AI Solutions roles: discovery, architecture, cost, privacy, stakeholders, escalations and production support.
Every question in this PDF is drawn from real candidate-reported interviews and published FDE hiring guidance — not generated or guessed. Where a question was reported nearly word-for-word, it is marked Verbatim. Where it reflects a documented, recurring pattern across multiple candidates, it is marked Reported Pattern.
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.
A complete lecture-style guide covering the theory of LLM cost economics, prompt/context caching, exact-match and semantic response caching, conversation history trimming, and tool-call memoization — with a full working cost-optimized agent you can run and measure.
A complete lecture-style guide covering the theory of observability for LLM agents, how tracing differs from logging, automatic and manual instrumentation with LangSmith, tagging and metadata, debugging a real trace, and monitoring an agent in production — with full working code.
A complete lecture-style guide covering the theory of multi-agent orchestration, how a supervisor routes requests to specialized subagents, the full architecture, complete working code, and a line-by-line explanation.
A complete lecture-style guide covering the theory of AI agent guardrails, input validation, output/schema validation, blocking disallowed actions, the full architecture, complete working code, and a line-by-line explanation.
A complete lecture-style guide covering the theory of Corrective RAG, how it builds on Agentic RAG by grading retrieval quality and rewriting queries, the full architecture, complete working code, and a line-by-line explanation.
A complete lecture-style guide covering the theory of Agentic Retrieval- Augmented Generation, how it differs from traditional RAG, the architecture, full working code, and a line-by-line explanation of building an agent that decides for itself whether retrieval is needed.
A complete lecture-style guide covering the theory, architecture, full working code, and a line-by-line explanation of building an agent that pauses for human approval before executing sensitive actions — a critical safety pattern for Agentic AI interviews.
A complete lecture-style guide covering the theory, architecture, full working code, and a line-by-line explanation of building a Planner– Executor agent — a core pattern for every LLM / Agentic AI coding interview.
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.
The Complete Roadmap — Beginner to Interview-Ready
JOINs | Window Functions | CTEs | Cohort Analysis | A/B Testing | Feature Engineering
Regression, Trees, SVM, Clustering, and Evaluation — the classical ML foundations every AI Engineer interview still tests
CNN, RNN, Backpropagation, and Transformers — the neural network foundations every AI Engineer interview builds on
LangChain is rarely tested as a standalone interview — it's almost always one part of a broader GenAI/LLM application interview (4–5 rounds: HM screen, technical round, take-home, system design). The questions below reflect the core concepts multiple 2026 sources confirm are commonly tested, including confirmed current facts like AgentExecutor's deprecation in favor of create_agent(). This is framed as commonly-tested material, not a guarantee of verbatim questions.
Retrieval Augmented Generation · End-to-End Architecture
Design a URL Shortener like bit.ly that allows users to shorten long URLs and redirect visitors to the original URL.
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
With Detailed Answers · Company Tags · Real Interview Tips
Production Agentic Systems | Real Scenarios
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.
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