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 chatbot must answer from product docs that change every week. Which approach fits best?

RAG: index the docs and retrieve relevant chunks at request time, This is a knowledge gap with facts that change often. RAG keeps the model frozen and updates knowledge by updating the index. Fine-tuning would force a retrain every time the docs move, and stuffing everything into the prompt does not scale.

What is the fundamental difference between fine-tuning and the other two approaches?

Fine-tuning changes the model's weights, while prompting and RAG only change what the model sees at request time, Prompting and RAG leave the weights frozen and change only the context. Fine-tuning is the only one that alters the model itself, which is why it is slower, needs labeled data, and is the wrong tool for facts that keep changing.

A team wants their model to always output a strict JSON schema, and prompting cannot make it reliable enough. They have 5,000 correctly formatted examples. Best move?

Fine-tune on the labeled examples to bake the output behavior into the weights, This is a behavior gap, not a knowledge gap, so retrieval does not help. With thousands of clean labeled examples and a stable target behavior, fine-tuning is the right tool. It makes the format reliable and shortens every prompt at the same time.

, engineers learning right now

Aryan from Bengaluru unlocked lifetime access · 3 minutes ago

Fine-Tuning vs RAG vs Prompting: Choosing Your Approach

Name: System Design Masterclass
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