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.

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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.

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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.

Why is a cross-encoder more accurate at judging relevance than the bi-encoder used for vector search?

It reads the query and the document together in one pass, so attention runs across both, instead of comparing two separately made vectors, A bi-encoder embeds the query and each document separately and compares the finished vectors, so it never sees them together and captures topic overlap more than true relevance. A cross-encoder joins the query and one document into a single input and runs them through the model together, letting attention span both texts. That joint reading is what makes it accurate, and because the score depends on the query it cannot be precomputed, which is why it is also slow.

In a retrieve-then-rerank pipeline, why does the first stage return around 50 candidates instead of just the 5 you will finally use?

To give the reranker a wide, high-recall set to reorder, since it can only promote chunks that were actually retrieved, The first stage optimizes for recall: cast a wide net so the right chunk is almost certainly inside the candidate set, even if it is ranked low. The reranker can only reorder what it is given, so if the right chunk is not in the 50, no reranking recovers it. Pulling more candidates and then keeping fewer, pass more and keep fewer, is what lets the accurate-but-slow reranker do its job on a set small enough to afford.

Your RAG answers are weak. You pull the raw top 50 for the failing queries and the correct chunk is not in the list at all. What should you do?

Fix the first stage: revisit chunking, the embedding model, or the query, because reranking cannot promote a chunk that was never retrieved, This is a recall problem, not a ranking problem. A reranker only reorders the candidates the first stage returned, so if the right chunk is not in the 50 it has nothing to promote. The fix lives upstream in retrieval: chunk size and boundaries, the embedding model, or the query. Reading the raw candidates is the cheap check that tells you whether you have a ranking failure (reranking helps) or a recall failure (it does not).

Which statement about the cost of reranking is accurate?

It adds one model pass over the candidate set per query, which costs latency and a reranker dependency, though the added time is often small next to generation, Reranking sits on top of your existing retriever with no re-indexing, but it is not free. It adds one cross-encoder pass over the candidates for every query, which costs latency and introduces a dependency, either a hosted API like Cohere Rerank or a self-hosted model like a BGE reranker. The exact latency depends on the model, candidate count, and hardware, and it is often small compared with the model's generation time, which is part of why the trade is usually worth it.

Reranking: The Highest-Return Change in Your RAG Stack

Name: System Design Masterclass
Availability: InStock

in Retrieval Rag

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