Design semantic search for a docs site
Embeddings, a vector index, and a golden set. The retrieval warm-up.
Tier 1 is a semantic search warm-up. Tier 4 is a multi-tenant GPU fleet on fire. Your tutor watches every whiteboard.
Embeddings, a vector index, and a golden set. The retrieval warm-up.
Sub-100ms LLM completions at keystroke rate. Latency is the product.
Grounded answers, guardrails, and a human handoff.
Classic ML under class imbalance. Choose the right metric first.
Long transcripts, structured notes, factuality checks. Async by nature.
The most-asked GenAI question. Retrieval quality, permissions, evals.
The classic ML flagship. Two-stage pipeline, features, cold start.
An adversary in the loop. Fresh features, skew, delayed labels.
The Meta staple. Features, leakage, calibrated probabilities, A/B.
Hybrid retrieval, rerank, generated answers. Search that cites.
The regression staple. Geospatial features, real-time traffic, freshness.
PDFs to typed JSON at scale. Schemas, retries, error analysis.
The flagship. Streaming, memory, moderation, GPU serving, cost.
Tab-completion latency + chat depth. Context is the scarce resource.
Judges, golden sets, CI gates. The system that grades the systems.
Anthropic's actual question. Batching, KV memory, goodput under SLOs.
Image embeddings at billions scale. Index choice, rerank, freshness.
Session-aware recs in seconds. Streaming features, freshness SLAs.
Multi-step search agents that compress the web into an answer.
Many models, many teams, one GPU fleet. Routing, isolation, unit cost.
An agent that acts on real systems. Permissions, sandboxes, injection.
A working product bleeding money. Find the multiplicative levers.
Every team, every model, one front door. Budgets, evals, governance.
20-second renders, GPU queues, content safety, cost per image.
A plausible AI architecture is on the board. Find what breaks first.