Computer vision is one of the most interview-tested areas of ML, especially at companies with physical products, autonomous systems, or […]
RLHF (Reinforcement Learning from Human Feedback) is the technique that transforms a raw language model into an assistant — the
Design an LLM inference API — the service that accepts user prompts and returns model completions, like the OpenAI API,
Design a recommendation engine like Netflix’s, Spotify’s Discover Weekly, or Amazon’s “Customers also bought.” Recommendation systems are one of the
Design a real-time collaborative document editor like Google Docs. This is one of the most technically nuanced system design problems
Embeddings are the lingua franca of modern AI applications. They power semantic search, RAG, recommendation systems, duplicate detection, and anomaly
Retrieval-Augmented Generation (RAG) is one of the most widely deployed LLM patterns in production. Understanding when to use RAG versus
Design an ad click aggregation system — the infrastructure that counts how many times each ad was clicked, detects fraud,
Design a monitoring and alerting system like Datadog, Prometheus + Grafana, or New Relic. This is a system design problem
One of the most common LLM interview questions in 2026: “Would you fine-tune a model or train from scratch?” Almost
Transformers are the architecture behind GPT, BERT, Claude, and every other major language model. Understanding how they work — especially
Design a Content Delivery Network like Cloudflare, Akamai, or AWS CloudFront. A CDN is infrastructure that most large-scale systems depend
Design a stock trading platform like NYSE, Nasdaq, or a brokerage like Robinhood. This problem is notable for its extreme
Train/test/validation splits are foundational — and routinely misunderstood. The most common mistake in applied ML is using the test set
One of the most common ML interview questions isn’t about a specific algorithm — it’s “how do you decide which
