Embeddings and Vector Databases Explained
6 min read Embeddings are the lingua franca of modern AI applications. They power semantic search, RAG, recommendation systems, duplicate detection, and anomaly […] Read article
AI/ML Interview Questions
← all topics
// ai-ml-interview-questions
AI/ML Interview Questions
ML systems, model design, and the applied-AI loop: RAG, agents, evals, and infrastructure.
AI/ML Interview Questions
What is RAG? Retrieval-Augmented Generation vs Fine-tuning
5 min read Retrieval-Augmented Generation (RAG) is one of the most widely deployed LLM patterns in production. Understanding when to use RAG versus Read article
AI/ML Interview Questions
How Transformer Models Work: Attention Mechanism Explained
6 min read Transformers are the architecture behind GPT, BERT, Claude, and every other major language model. Understanding how they work — especially Read article
AI/ML Interview Questions
Fine-tuning LLMs vs Training from Scratch: When and How
6 min read One of the most common LLM interview questions in 2026: “Would you fine-tune a model or train from scratch?” Almost Read article
AI/ML Interview Questions
How to Choose Between Machine Learning Models
5 min read One of the most common ML interview questions isn’t about a specific algorithm — it’s “how do you decide which Read article
AI/ML Interview Questions
Train/Test/Validation Split: The Right Way and Common Mistakes
6 min read Train/test/validation splits are foundational — and routinely misunderstood. The most common mistake in applied ML is using the test set Read article
AI/ML Interview Questions
Feature Selection and Dimensionality Reduction: PCA, Lasso, and Tree Importance
6 min read Feature selection and dimensionality reduction are how you fight the curse of dimensionality — the phenomenon where models trained on Read article
AI/ML Interview Questions
Handling Imbalanced Datasets in Machine Learning
5 min read Imbalanced datasets — where one class dramatically outnumbers another — are the norm in production ML, not the exception. Fraud Read article
AI/ML Interview Questions
Cross-Validation Strategies: K-Fold, Time Series, and Nested CV
5 min read Cross-validation is how you estimate a model’s generalization performance before deploying it. Getting this wrong — especially data leakage — Read article
AI/ML Interview Questions
Classification Metrics: Precision, Recall, F1, and AUC-ROC
6 min read Classification metrics are one of the most frequently misused concepts in ML interviews. The wrong answer: “I use accuracy.” The Read article
AI/ML Interview Questions
How Backpropagation Works: The Chain Rule in Neural Networks
6 min read Backpropagation is the algorithm that makes training deep neural networks possible. Every interviewer for ML engineering or research roles expects Read article
AI/ML Interview Questions
Overfitting and Regularization: L1, L2, Dropout, and Early Stopping
6 min read Overfitting is the most common failure mode in machine learning. Every ML interview will test your ability to recognize it Read article
AI/ML Interview Questions
Bias-Variance Tradeoff: Underfitting, Overfitting, and How to Fix Both
5 min read The bias-variance tradeoff is one of the first concepts asked in any machine learning interview. It underpins model selection, regularization, Read article
AI/ML Interview Questions
Gradient Descent Explained: SGD, Adam, and When to Use Each
6 min read Gradient descent is the engine behind nearly every machine learning model trained today. If you are interviewing for an ML Read article
