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.

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

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.

A tree where each path from root to node represents a string prefix -- O(L) lookups where L = string length, independent of number of strings.

Count distinct elements in a stream using O(log log n) memory -- with ~2% error rate, vs O(n) for an exact set.

The neural network zoo: Different architectures for different data modalities—images, sequences, and everything in between.

A tree of hashes where each parent = hash(left child + right child) -- detect exactly which data blocks differ between two systems in O(log n).