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.

Is sending user data to an LLM provider a compliance risk?

Yes, because the prompt leaves your trust boundary and becomes personal data processed by a third party in some jurisdiction, retained for a window you did not set. Regulators treat mishandling it as a breach, so you redact PII before it crosses the boundary, choose a provider tier and region that meet your retention and residency rules, and put the vendor's commitments in a data processing agreement.

How do you redact PII before sending a prompt to an LLM?

Run a detection and masking pass on the fully assembled payload before it reaches the provider or any log. Structured identifiers like emails, phone numbers, and card numbers are caught with regex plus a checksum such as Luhn for cards, while names and addresses need a named-entity model like Microsoft Presidio. Mask values the model never needs, tokenize values a later step must restore, and keep the raw record out of the prompt where you can.

Why are LLM logs and eval sets a common source of PII leaks?

Because teams log the full prompt and completion for debugging and build eval sets from real production traffic, which copies the exact PII into systems nobody classified as sensitive. The fix is ordering: redaction runs first, so the provider, the logs, the trace store, and the eval builder all see only the redacted copy, and prompt logs get the same data classification as the source table.

Does the right to be forgotten apply to embeddings and fine-tuned models?

Yes for embeddings, and it is much harder for fine-tunes. An embedding of personal data is still personal data, since text can be substantially reconstructed from its vector, so deleting the source row while keeping the embedding does not honor a deletion request. Removing one person's data from a trained model is machine unlearning, an open problem, which is why you should not fine-tune on raw PII in the first place.

What is data residency for LLM apps and why does it matter?

Data residency is the requirement that personal data be processed in a specific region, such as inside the EU, and a default provider endpoint that routes to US regions does not meet it regardless of retention policy. Providers offer regional processing options, so your endpoint becomes a per-tenant routing decision, and every store that holds copies, including logs and vector stores, inherits the same residency constraint.

What do GDPR and the EU AI Act require for LLM applications?

The GDPR's data minimization and purpose limitation principles justify redaction and restrict reusing production data as eval sets, and its right to erasure requires deletion to reach every store. The EU AI Act adds risk-based obligations, lighter for most features and heavier for uses it designates high risk, phasing in across 2025 to 2027. This is a framework summary, not legal advice, so get a qualified assessment for your product and region.

Your team redacts PII from prompts before calling the model, but incident counts of exposed personal data keep climbing. The redaction pass is correct. Where is the leak most likely coming from?

Your logs, traces, and eval sets capture the raw pre-redaction payload, so the same PII sits in systems nobody classified as sensitive, This is the most common leak in LLM systems and it is an ordering bug, not a detection bug. If logging captures the payload before redaction, or the trace store and eval set are built from raw traffic, your observability stack now holds plaintext names, emails, and card numbers, often replicated to a third-party logging vendor. Redaction has to run first so every downstream consumer sees only the redacted copy, and prompt logs need the same data classification as the source table.

A card detector masks any 16-digit number it finds. Support agents complain that order references are being destroyed in transcripts. What is the fix that keeps card protection without the false positives?

Run the Luhn checksum on each candidate and only redact runs that pass, since real card numbers pass Luhn and order ids generally do not, A 16-digit run is not enough to identify a card, because order ids and tracking numbers are also 16 digits. Real card numbers satisfy the Luhn checksum, so validating each candidate before redacting keeps genuine cards masked while leaving order references intact. This is exactly what the redaction playground does, and it is why the order reference in the example survives while the card is masked.

A user exercises their right to erasure. Your team deletes their rows from the primary database and confirms the request is complete. What is most likely still wrong?

The same records were copied into the vector store, prompt logs, cache, eval sets, and the provider, and each is a separate delete that must succeed, In an LLM system one record has been copied into stores that do not think of themselves as holding user data. The message was embedded into the vector store, logged as a prompt and completion, cached, possibly captured in an eval set, sent to a logging vendor, and passed to the provider. Erasure has to fan out to all of them, and the compliance test is that no rows for that user remain in any store you control, which is why the retention sweep iterates over stores rather than a single table.

Why is deleting a user's fine-tuning data fundamentally harder than deleting their database row, and what is the practical response?

A trained model can memorize and later emit training examples, and removing one example without retraining is an unsolved problem, so you avoid it by not fine-tuning on raw PII, A model trained on data spreads that data through its weights and can later emit specific examples it memorized, as shown in the training-data-extraction work. Removing one person's contribution without retraining from scratch is machine unlearning, an open research problem with only approximate methods. Deleting the training file does not un-train the model, so the practical response is to redact or tokenize the training set up front, keeping raw PII out of the model where an erasure request could never fully reach it.

PII and Compliance for LLM Apps: Redaction, Retention, and Data Boundaries

Name: System Design Masterclass
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