Is this a video course?

No. This is an interactive, slide-based learning platform. Each lesson has rich text, animated diagrams, live code editors, and quizzes. You learn by reading, interacting, and doing, not by watching videos passively.

How long do I have access?

Forever. Both pricing tiers are one-time payments with lifetime access. This includes all current 766 lessons and any future content we add.

What level of experience do I need?

None. We start from absolute basics like 'What is latency?' and build up to distributed consensus protocols. The Foundation level assumes zero prior knowledge of system design.

How much does the system design course cost?

7.99 US dollars for lifetime access globally, or 499 Indian rupees for lifetime access in India. One-time payment, no subscription, no hidden fees. 11 lessons are free with no signup required.

What technologies are covered?

Everything from DNS and load balancers to Kubernetes, Kafka, distributed databases, consensus protocols, stream processing, security architecture, and observability. We cover principles and real-world implementations used at Netflix, Google, Amazon, Uber, Stripe, and more.

Is this useful for system design interview preparation?

Yes. The lessons are structured around the exact topics asked in system design interviews at FAANG and top-tier companies. Interactive diagrams help you practice whiteboard-style explanations. Covers everything from URL shortener design to distributed payment systems.

How is this different from ByteByteGo or Educative?

766 interactive lessons (4x more than most competitors), 16 different diagram types that build step by step, real production examples from Netflix, Google, Amazon, Uber, and Stripe, and lifetime access for a one-time payment of $7.99 instead of annual subscriptions costing 100 to 200 dollars per year.

A team scores two model versions with an LLM judge in a pairwise A vs B comparison. Version B wins clearly. What is the cheapest check before trusting that result?

Run the same comparison with the two answers in the swapped order and see if the winner holds, This is position bias. Judges favor whichever answer sits in a fixed slot. Running both orderings and comparing costs one extra call. If the winner flips when you swap A and B, the order decided it, and the pair should be scored as a tie rather than a real win. A bigger judge does not fix a structural bias.

Why is self-preference bias a specific risk when choosing a judge model?

A judge tends to rate outputs from its own model family higher, so a model grading its own family's output inflates the score, Self-preference, or self-enhancement, is the tendency of a judge to score text from its own model family higher than it deserves, documented in work at EMNLP 2025. If a GPT model judges GPT output, the result is inflated. The fix is to judge with a different model family and never let a model be the sole grader of its own outputs.

You send a judge the exact same output and rubric five times and get three PASS and two FAIL verdicts. What is the right reading of this?

A single judge call is a noisy sample; on hard cases judge agreement can approach a coin flip, so you sample more, lower temperature, and prefer binary verdicts, This is run-to-run instability, and it is expected, not a bug in your setup. RAND's 2026 Judge Reliability Harness found no judge uniformly reliable across benchmarks. A single call is one noisy sample. Lower the temperature, sample more than once, and prefer stable binary verdicts over a fine-grained scale.

DoorDash improved a fuzzy relevance judge. What was the move that worked?

They decomposed the one fuzzy question into several concrete binary facet checks, so structure beat trying to build a smarter judge, DoorDash split a vague 'is this relevant' judgment into concrete yes-or-no facet checks, such as whether the result matches the query intent and whether the item is available. Binary facet checks are more stable across runs and auditable facet by facet. The lesson is that structuring the task beats reaching for a smarter judge model.

LLM-as-Judge and Its Failure Modes

Name: System Design Masterclass
Availability: InStock

in Evals

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