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.

Why does retrieval-augmented generation reduce hallucination compared to a raw LLM?

It fetches relevant passages and instructs the model to answer only from that retrieved context, RAG moves knowledge out of the model's frozen weights and into an external index. At query time it retrieves the relevant chunks and tells the model to answer strictly from them, which grounds the answer instead of letting the model invent one.

What is the job of the reranker in a RAG pipeline?

To rescore the wide set of retrieved candidates and keep only the few that best answer the query, Vector similarity is fast but coarse, so retrieval returns a wide set (say 30 candidates). A cross-encoder reranker reads the query and each passage together, scores true relevance, and keeps the best few. It is usually the single biggest quality lever in a RAG system.

Why does a semantic cache save so much more than an exact-match cache in a RAG system?

It matches questions by meaning, so near-duplicate questions hit the cache and skip the expensive LLM call, Users ask the same thing a thousand different ways. A semantic cache keys on the embedding of the question, so "reset my password" and "change my password" match the same entry. A hit returns in milliseconds at zero cost, versus a full pipeline run dominated by a one-to-three-second LLM call.

, engineers learning right now

Aryan from Bengaluru unlocked lifetime access · 3 minutes ago

Production RAG and LLM Systems: Grounding Models in Your Own Data

Name: System Design Masterclass
Availability: InStock

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