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.

Both models scored recall@1 of 1.00 on the fifteen-document corpus. What does that result actually tell you?

The test was too easy to separate them, so it ranked nothing, A test that everything passes has not ranked your options, it has only failed to separate them. When thirty near misses were added, a real difference appeared: 0.70 for the free model and 0.80 for the paid one. The easy corpus could not show that gap at all.

Thirty near misses cost 30 points of recall@1, while two thousand unrelated documents cost 20. Which property made the thirty so damaging?

They shared the topic and the words of the real answer but described a different outcome, A near miss lands close to the question precisely because it is about the same thing. The clause that reverses the meaning, such as 'the primary had space', is a small part of the text and moves the vector very little, while the shared topic dominates the comparison.

In the near-miss run recall@1 was 0.70 and recall@3 was 1.00. In the unrelated-document run they were both 0.80. What do those two patterns mean?

The near-miss run has an ordering problem; the unrelated-document run has a retrieval problem, When recall@3 stays at 1.00, the answer is still being retrieved and is only ranked too low, which a reranker or a metadata filter can fix. When recall@3 falls with recall@1, the answer is dropping out of the window altogether, which needs chunking, keyword search or a bigger k. Retrieving more cannot fix an ordering problem.

The same 25 embeddings on Bedrock measured 48.47 seconds one way and 2.99 seconds another. What changed?

The first run started a new process per call; the second reused one client and sent eight requests at a time, Same service, same model, same region. The 48 seconds was dominated by interpreter startup and connection setup inside the aws CLI, paid 25 times over. A benchmark that includes your own harness costs is measuring your harness, not the service.

Adding 'search_document: ' and 'search_query: ' took recall@1 from 0.50 to 0.70 with no other change. Why is this class of bug so easy to miss?

The call succeeds and returns a valid vector, so nothing anywhere reports an error, There is no failure to catch. You get 768 numbers back and everything downstream works. Retrieval is simply quietly worse than it should be. The prefixes are also model specific: Titan takes none, so copying the fix across would be a new bug.

Choosing an Embedding Model: Why the Leaderboard Is Measuring Someone Else's Documents

Name: System Design Masterclass
Availability: InStock

in Retrieval Rag

This lesson is part of the AI Engineering course. Unlock the full course, a separate one-time payment, for lifetime access.

One-time payment. Lifetime access. No subscription.

Global

$20/lifetime

One-time payment via PayPal

Get Lifetime Access

5 Foundation lessons free, try before you buy

India Special

India

₹999/lifetime

One-time via UPI, cards, or net banking

Get Lifetime Access

5 Foundation lessons free, try before you buy

“The interactive diagrams alone are worth way more than the price.”

Anish K., Senior Engineer at a Bengaluru startup

Every ML Platform lesson, Foundation to Advanced

Real architecture diagrams with real tool logos

MLOps, LLMOps, and data engineering in depth

Quizzes with instant feedback

Built and reviewed by senior engineers

Lifetime access, all future content included

The first 5 Foundation lessons of this course are free, no payment needed. Try them now