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.

How do you know a new model version did not silently break your prompts?

You run a versioned eval set in CI against both the old and new model snapshot and diff the per-facet pass rates, so a behavior change shows up as a failed build instead of a user complaint. The break is silent because your request body is unchanged and no error is raised; only re-scoring your own eval set against the two snapshots surfaces it.

Why do prompts break when the model version changes even though nothing errors?

Because most production code calls the model by a floating alias like gpt-4o rather than a pinned dated snapshot, and providers repoint that alias to newer snapshots. The new snapshot can format output differently, refuse prompts it used to answer, or shift tone, all with a normal 200 response, so the failure lives in the content and never trips an exception or a latency alarm.

What is benchmark contamination in LLM evaluation?

Benchmark contamination is when the eval set leaks into how you tune the system, so the score measures memorization instead of quality. It happens when an author edits a prompt to pass specific eval cases, or pastes an eval case into the prompt as a few-shot example, which makes the score climb while performance on genuinely unseen traffic stays flat and then collapses in production.

What is holdout hygiene and why do you need a sealed eval set?

Holdout hygiene means never tuning on the set you measure with, which forces at least two sets: a development set you tune against freely and a sealed holdout nobody reads case by case. The sealed set gives an honest pass rate because it never influenced a decision, so when the CI set says 0.95 and the sealed set says 0.82, the CI set is overfit and the sealed set is the truth.

How do you set a regression threshold that catches real drops without failing on noise?

Size the threshold to the sampling noise, not by feel: a pass rate p on n cases has a standard deviation of sqrt(p times (1 minus p) divided by n), so fail only when the drop exceeds about two of those, or when a two-proportion test calls it significant. For a rate near 0.85 on 200 cases that band is about 0.05, so a drop to 0.83 is noise and a drop to 0.78 is a real regression.

How do you handle run-to-run variance from an LLM judge in CI?

Grade each flaky case several times and take the majority verdict instead of trusting a single stochastic call, which pulls each case toward its true side and shrinks the aggregate jitter. Use thresholds and rates rather than exact-match reproducibility, since a stochastic judge cannot be made exactly repeatable, and reserve multi-sample voting for borderline cases to control the added judge cost.

Your production code calls the model as gpt-4o rather than gpt-4o-2024-08-06. The provider promotes a new snapshot behind the gpt-4o alias. What is the risk?

Behavior can change with no error raised, so a prompt tuned to the old snapshot breaks silently, An undated alias tracks whatever snapshot the provider currently ships, and providers move that pointer. When it rolls forward your request body is unchanged, so no exception is raised and latency looks normal, but the model behind the alias is different. A prompt fitted to the old snapshot can now format output differently or refuse prompts it used to answer, and the only thing that catches it is re-running a pinned eval set across the two snapshots.

Between two model versions, your overall eval pass rate moves from 0.93 to 0.92, but the format facet fell from 0.98 to 0.80 while three other facets rose. Why did the aggregate hide the regression?

The aggregate is an average, so gains on other facets masked the large drop on one, The overall number is an average across facets, so a large fall on one facet can be canceled by small rises on the others, leaving the average nearly flat. The format facet dropped eighteen points, but grounded, answered, and safe all rose, so the aggregate moved only one point. A per-facet gate compares each facet against its own margin and fails on the format drop, which is why you diff per facet rather than watching one blended score.

Your CI eval set reports 0.96, but a sealed holdout that nobody has tuned against reports 0.82 on the same system. What is the most likely explanation?

The CI set has been overfit or contaminated, and the 0.82 is the honest estimate, A sealed holdout has never influenced tuning, so its pass rate is unbiased, while the CI set is one you run and tune toward repeatedly, which inflates its number through overfitting or outright contamination. A large gap in favor of the CI set is the signature of that inflation, so the 0.82 is the estimate to trust. The fix is to stop tuning against the CI set and, if it is burned, rotate in a fresh holdout sampled from recent traffic.

A baseline facet passes at 0.85 on 200 cases. A new run scores 0.83. Should the gate fail the build?

No, because 0.83 is within about two standard deviations of sampling noise, so it is not distinguishable from no change, The sampling standard deviation of a 0.85 rate on 200 cases is sqrt(0.85 times 0.15 divided by 200), about 0.025, so two standard deviations is about 0.05 and the noise band reaches down to roughly 0.80. A run at 0.83 sits inside that band, so it is not evidence of a real drop, and failing on it would be a false alarm that trains the team to ignore the gate. A drop to 0.78 or below would clear the band and count as a real regression.

Prompt CI and Benchmark Integrity: Catch the Silent Break

Name: System Design Masterclass
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