Comprehensive AI/ML and LLM interview questions covering fundamental concepts, algorithms, and practical applications. AI/ML Fundamentals Interview questions covering core machine learning concepts, algorithms, and best practices: Easy: Supervised/unsupervised learning, overfitting, bias-variance tradeoff, basic …

Easy-level LLM and Agentic AI interview questions covering fundamentals, prompting, and basic agent concepts. Q1: What is a Large Language Model (LLM)? Answer: Definition: Neural network trained on massive text data to predict next tokens, enabling text generation and understanding. Key Characteristics: Large: Billions …

Hard-level LLM and Agentic AI interview questions covering multi-agent systems, production optimization, and advanced architectures. Q1: Design a multi-agent system with agent communication and coordination. Answer: How Multi-Agent Systems Work: Multiple specialized agents collaborate, communicate, and coordinate to …

Medium-level LLM and Agentic AI interview questions covering agent architectures, RAG optimization, and production systems. Q1: Design and implement a ReAct (Reasoning + Acting) agent. Answer: How ReAct Works: ReAct alternates between reasoning (thinking) and acting (using tools) to solve problems. Pattern: 1Thought: I …

Practical recipes for building LLM applications with LangChain: prompts, chains, agents, memory, and RAG. Installation 1# Core LangChain 2pip install langchain langchain-community langchain-core 3 4# OpenAI 5pip install langchain-openai 6 7# Other providers 8pip install langchain-anthropic # Claude 9pip install …

Graph-based Retrieval-Augmented Generation for enhanced context and relationship understanding. Core Idea Graph RAG represents knowledge as a structured graph (entities and relationships) rather than flat document chunks, enabling multi-hop reasoning and relationship-aware retrieval. Mathematical Foundation Traditional …

Simple stdin chatbot using LangChain with tool calling (OpenRouter). Installation 1pip install langchain langchain-openai python-dotenv Environment Setup 1# .env 2OPENROUTER_API_KEY=your_api_key_here 3OPENROUTER_MODEL=openai/gpt-4-turbo-preview Basic Chatbot 1import os 2from dotenv import load_dotenv 3from …

Retrieval-Augmented Generation techniques for enhancing LLM responses with external knowledge. Core Idea RAG combines retrieval from external knowledge bases with LLM generation to produce accurate, up-to-date responses without retraining the model. Mathematical Foundation The core retrieval mechanism uses cosine …