Map term to list of documents containing it -- the core data structure powering every full-text search engine.

A 2D hash table that estimates frequency of any element in a stream using fixed memory -- with bounded overcount error, never undercount.

The 4-step method for any system design interview: Requirements, Capacity, Design, Deep Dive.

Semantic search at scale: Store high-dimensional embeddings, find similar documents in milliseconds.

Three approaches to index location data for find things near me queries -- each with different trade-offs.

The difference between deployed models and production ML systems: Continuous measurement of model and data health, drift detection, automated alerts, and feedback loops to trigger retraining before users are impacted.

A layered linked list with express lanes -- O(log n) average search/insert using randomization, without the complexity of balancing a tree.

Safely shipping models to production: Packaging, staged rollout (shadow mode, canary, A/B test), infrastructure, and strategies to minimize risk and measure impact.

Grounding LLMs in knowledge: Combine document retrieval + LLM generation to answer questions with up-to-date, verifiable information.

Circuit Breaker fails fast when downstream is broken, preventing cascading failures. Bulkhead isolates resources per service, preventing one slow dependency from starving others.