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.

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.

What is a prompt registry?

A prompt registry is a model registry but for prompts: every prompt is a named artifact with immutable versions, one tagged as production, and the app looks it up by name and stage instead of carrying the text. Pulling the prompt out of the code and into a versioned catalog is what makes iteration, testing, rollback, and lineage possible.

Why should prompts be versioned instead of hardcoded?

Because a prompt is the most important business logic in an LLM app, and a wording change is a production change that can silently break quality, cost, or format. Versioning lets you iterate without a deploy, test before shipping, roll back in seconds by repointing a tag, and trace any bad answer to the exact prompt that produced it.

How should a new prompt version reach production?

It should travel a promotion path like a code deploy: draft, then staging where it must clear an eval gate and score no worse than the current production version, then a canary on a small slice of real traffic, and only then does the production tag move. A version that fails the gate or the canary bounces back to the author instead of becoming an incident.

What belongs inside one prompt version besides the text?

Everything that decides what the model does and how you grade it: the template wording and any few-shot examples, the named input variables, the full model contract (provider, model name, temperature, token cap, stop sequences, tool definitions), and the grading trail (the eval suite reference, thresholds, owner, and changelog). If any of these changes without a new version, a comparison between two versions is no longer fair.

What is prompt lineage?

Prompt lineage is logging, for every request, the prompt name and version, the rendered input, the model and its settings, and the output, so a vague complaint becomes a mechanical investigation. You look up the request id, find the version that answered, diff it against the previous one, and repoint the tag if it is the culprit.

Why include the prompt version in the cache key?

Because if the cache key ignored the version, shipping a new prompt version would keep returning the old version's cached answers and you would never know why the change is not taking effect. Baking the version and model into the key gives every prompt change a fresh cache namespace, keeping caching both cheap and correct.

What is the core idea behind a prompt registry?

Treating each prompt as a named, versioned artifact the app looks up at runtime, like a model registry for prompts, A prompt registry treats prompts the way a model registry treats models: every prompt is a named artifact with immutable versions, one tagged as production, and the app requests it by name and stage instead of carrying the text.

Why should a new prompt version go through an eval gate and a canary before reaching all users?

Because a wording change is a production change that can silently break output quality, cost, or format, and the gate catches regressions before real users see them, A prompt edit changes what the model does. An eval suite checks it against fixed inputs and blocks regressions versus production, and a canary tests it on a small slice of real traffic, so a bad change bounces back to the author instead of becoming an incident.

Besides the template text, what must a prompt version pin to keep comparisons between versions fair?

The model contract: provider, model name, temperature, token cap, and stop sequences, The same wording at temperature 0.9 behaves differently than at 0.3, and a silent model upgrade changes behavior underneath you. Pinning the model and its parameters inside the version is what makes a golden-set comparison between two versions honest.

Why is the prompt version included in the prompt cache key?

So that shipping a new prompt version does not serve the old version's cached answers for the new prompt, If the cache key ignored the version, a new prompt version would keep returning the previous version's cached completions. Baking the version (and model) into the key gives every prompt change a fresh cache namespace, keeping caching both cheap and correct.

Prompt Management and Versioning: Treat Prompts as Production Code

Name: System Design Masterclass
Availability: InStock

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