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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.

How much does the system design course cost?

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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.

Can a classifier reliably block prompt injection?

No, because input is open natural language and a classifier is a probabilistic filter over an infinite space of phrasings, so an attacker can reword, translate, encode, or split the instruction until it slips past. EchoLeak, tracked as CVE-2025-32711, began by defeating exactly Microsoft's XPIA injection classifier, so input filtering is one thin layer and never the wall.

What is spotlighting in prompt injection defense?

Spotlighting, formalized by Microsoft Research, gives the model an explicit signal for which tokens are untrusted data by delimiting, datamarking, or encoding the untrusted span. It makes injection harder and Microsoft reported attack success dropping sharply, but a strong model can still be talked past the boundary, so it is a first layer rather than a complete fix.

What is the dual-LLM pattern?

The dual-LLM pattern, described by Simon Willison, splits work between a privileged planner that sees only trusted input and symbolic variables, and a quarantined reader that processes untrusted content but holds no tools. The rule that makes it safe is that the model that can act never reads untrusted text, and the model that reads untrusted text can never act, so injecting the reader only returns bad data into a variable.

What is CaMeL and how does it defeat prompt injection?

CaMeL, from Google, Google DeepMind, and ETH Zurich, has the privileged model emit a full plan as code from the trusted query before any untrusted data is fetched, so later data cannot change the control flow. A custom interpreter then tracks each value's provenance as a capability and blocks untrusted data from flowing into a sensitive sink, which on the AgentDojo benchmark gave provable security on 77 percent of tasks against 84 percent undefended.

Why use an egress allow-list instead of a denylist for agent output?

Because a denylist must name every bad domain and fails against one registered an hour before the attack, while an allow-list names the few destinations the agent legitimately needs and refuses everything else by default. That way a freshly registered exfiltration domain is blocked precisely because it is unknown.

When should a human approve an agent action?

A human should approve any action that cannot be undone, such as sending money, deleting records, or emailing an external address, because an injected instruction cannot click the approve button. The human must confirm the concrete action with its real arguments rather than a vague intent, and a confirmation nobody requested is itself a detection signal.

Your team proposes to stop prompt injection by running a classifier that blocks inputs containing attack phrases like 'ignore previous instructions.' Why is this insufficient on its own?

Input is open natural language, so an attacker can reword, translate, encode, or split the instruction until it slips past, and one that gets through is enough, A classifier is a probabilistic filter over an infinite space of phrasings. It catches the obvious payloads, which is why it demos well, but attackers can paraphrase, encode, or hide the instruction. EchoLeak (CVE-2025-32711) started by defeating exactly Microsoft's XPIA classifier. Input filtering is one thin layer, never the wall.

In the dual-LLM pattern, what is the single rule that makes a successful injection of the quarantined model harmless?

The model that can call tools never reads untrusted text, and the model that reads untrusted text has no tools and cannot act, The privileged planner sees only the trusted request and symbolic variables, so there is nothing in its context to hijack it. The quarantined reader processes untrusted content but holds no tools, so injecting it only returns bad data into a variable. Separating the ability to act from the exposure to untrusted text is what contains the attack.

Why does CaMeL have the privileged model emit a fixed plan as code before any untrusted data is fetched, rather than improvising step by step?

So the control flow is fixed from trusted input alone and untrusted data can never change what the program does; the interpreter then enforces capabilities on each tool call, CaMeL derives the plan from the trusted query up front, so no data that flows back later can alter the control flow. A custom interpreter runs the plan and tags every value with its provenance as a capability, checking capabilities before each tool call so untrusted data cannot reach a sensitive sink. On AgentDojo it solved 77 percent of tasks with provable security versus 84 percent undefended.

For the exfiltration leg of the trifecta, why is an egress allow-list preferred over a denylist of known-bad domains?

A denylist must name every bad domain and fails against a freshly registered one, while an allow-list refuses everything not explicitly approved, so an unknown exfiltration domain is blocked by default, You cannot enumerate every malicious domain, and attackers register new ones on demand, so a denylist always trails the threat. An allow-list enumerates the few destinations the agent legitimately needs and denies the rest, which means a novel exfiltration URL is blocked precisely because it is unknown. Pair it with stripping remote markdown images and links to close the zero-click channel.

Defending Against Prompt Injection: The Layered Playbook

Name: System Design Masterclass
Availability: InStock

in Security

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