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How to Design a System

Faced with 'design something big', most people freeze or jump straight to a database. A good doctor never works that way: they run the same routine every time. Here is that routine for systems, in six steps.

Day one of the System Design series. Before any load balancers or databases, you need a way to think. This is the six-step routine, scope, estimate, API, data model, high-level design, then deep dive, walked through on a real example (a link shortener). With an everyday analogy, a hand-drawn diagram, and an interactive visualizer that shows the routine turning a vague prompt into a design.

• System Design

How Search Works

Every search engine does the same four things. Once you see them, the rest is variations.

The opening piece of a search series. Search is four steps that never change (index, parse, match, rank) and a hundred choices about how to do each. This part lays out the inverted index, boolean retrieval, why matching is not ranking, and the precision-recall tradeoff every later technique is a bet on, with worked examples throughout.

• Search

Peer-reviewer for International Conference on Machine Learning

Peer reviewer for the Trustworthy AI for Good (AI4GOOD) workshop at ICML 2026.

How to Actually Evaluate an LLM System

What an eval is, the types of eval, and the step-by-step for running one that tells you something true.

Eval is how you turn a fuzzy, open-ended LLM system into something you can measure. This is a practical guide to the types of eval, from exact match to LLM-as-judge to human review, and the six-step procedure for running one you can actually trust.

• LLM Ops & Eval

Judging at Google I/O Kickoff: Pre-World Cup Hack

Judged the Google I/O kickoff pre-World Cup hack (San Francisco).

Artifact evaluator for Computer Aided Verification

Artifact evaluator for CAV 2026 (Computer Aided Verification).

Judging at AI Agents Hackathon #33

Judged AI Agents Hackathon #33, a virtual hackathon run by Open Source for AI and the r/AI_Agents community.

Beyond the Vector

GraphRAG local and global search, the two multimodal strategies, the lineage from RETRO to CAG, each with its own analogy and visualizer.

The last piece of the RAG series: when flat vectors are the wrong shape for the knowledge. GraphRAG with local search and global search as separate ideas, multimodal RAG's two strategies (shared embedding space and translate-to-text), the family tree (RAG-Sequence, RAG-Token, REALM, Fusion-in-Decoder, RETRO, Atlas), the long-context-versus-RAG debate, and cache-augmented generation. Each with an everyday analogy and an interactive visualizer.

• RAG

RAG That Thinks

Iterative, adaptive, FLARE, Self-RAG, corrective, speculative, and agentic RAG, each with its own analogy and its own visualizer.

The control-flow axis of RAG, pattern by pattern, each with an everyday analogy and an interactive visualizer: iterative and multi-hop RAG, adaptive RAG, FLARE, Self-RAG, corrective RAG (CRAG), speculative RAG, and agentic RAG. How each adds a loop, and then adds judgment to the loop, and how to stop at the least autonomous pattern that answers your questions.

• RAG

Peer-reviewer for International Conference on Machine Learning

Peer reviewer for the Agents in the Wild (AIWILD) workshop at ICML 2026.