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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.

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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.

What is LLM observability?

LLM observability is the practice of recording, per request, what an LLM application actually did: the exact prompt sent after templating and retrieval, the model and version that served it, token counts split into input, output, and cached, latency for each step, tool calls with their results, and the final output, all linked by one trace id. It exists because LLM apps fail behind a 200 OK, returning a wrong answer with a healthy status code, so transport-level monitoring cannot see the failures that matter.

What should I log for every LLM request?

Seven things: the fully rendered prompt as sent, the requested and served model versions, input, output, and cached token counts as separate numbers, per-step latency for retrieval, model, and tool calls, every tool call with arguments and results, the final output text, and a stable trace id threading all of it. The rendered prompt is the one teams most often skip and most often need, because bugs like an empty retrieval context are invisible in the prompt template and obvious in the rendered string.

What are the OpenTelemetry GenAI semantic conventions?

They are the standard attribute names for LLM telemetry, under the gen_ai.* prefix: gen_ai.operation.name, gen_ai.request.model, gen_ai.usage.input_tokens and output_tokens, gen_ai.tool.name, with spans named like chat gpt-4o. Using them makes traces portable across observability backends. The conventions are still marked in development and attribute names have changed between versions, so pin the semconv version your instrumentation emits and treat upgrades as migrations.

What is the difference between LLM observability and LLM evaluation?

Observability records facts about each request: what was sent, what came back, how long it took, what it cost. Evaluation judges quality: whether the output was grounded, relevant, and correctly refused. They connect through online evaluation, where judges score a sample of stored traces and write the scores back against the trace id, so quality trends appear on the same dashboards as latency and cost, and every quality alert can produce the exact traces behind it.

What is self time in a trace and why does it matter?

Self time is a span's duration minus the durations of its child spans, the time the span spent on its own work rather than waiting for children. It matters because a parent span's total includes everything beneath it: a 180 ms retrieval step whose children cover 170 ms has only 10 ms of its own. Ranking spans by self time instead of total time stops you from optimizing a step whose time was never its own.

Do I need to store full traces for every request?

No, and at scale you should not, because full LLM traces carry kilobytes of prompt and output text with PII inside. The standard policy is tail-based: keep full traces for all errors, refusals, timeouts, latency outliers, and user-flagged requests, keep a small random sample of normal traffic as a baseline, and keep only the span skeleton, timing, tokens, models, and status, for the rest. Redact PII in the collector before anything is stored.

A user reports a confidently wrong answer. The request logged 200 OK at normal latency, and error dashboards are green. Which recorded artifact is most likely to reveal the root cause?

The full trace, including the rendered prompt as sent and the retrieval span's result count, LLM failures hide in content, not transport, so status codes, durations, and host metrics all read healthy. The template in the repo is not what the model saw; the rendered prompt is, and a failure like an empty context block from a broken retrieval step is visible only there, next to a retrieval span showing zero results. This is why the rendered prompt and per-step spans are captured on purpose for every request.

In a trace, the retrieve span shows 180 ms total and its two children (embed, search) cover 170 ms of it. The generation span shows 1195 ms with no children. Where should optimization effort go, and why?

The generation span: its 1195 ms is all self time, while retrieve's self time is only 10 ms, A parent's total includes its children, so retrieve's own work is 180 minus 170, just 10 ms. Self time, total minus children's totals, is the number that says where work actually happened. The generation span is a leaf, so its 1195 ms is entirely its own, and it dominates the request. Ranking spans by total time would have sent you tuning a step that owns 10 ms.

Why emit LLM telemetry using the OpenTelemetry gen_ai.* semantic conventions instead of custom attribute names like myapp.tokens_in?

Standard names make traces portable across backends, so dashboards and queries survive a vendor change; you pin the semconv version because the conventions are still evolving, The conventions are a shared vocabulary, not a provider requirement or a collector rule. Their value is portability: gen_ai.usage.input_tokens means the same thing in Langfuse, Jaeger, or Datadog, so your telemetry outlives any single backend. The honest caveat is that the conventions are marked in development and have renamed attributes between versions, which is why you pin and record the semconv version you emit rather than chasing latest.

How do online evaluation scores connect to observability in a production LLM system?

Judges sample stored traces asynchronously and write scores back against the trace id, so quality becomes a queryable column joined to latency and cost, Nothing judges inline; users never wait on quality control. A sampler pulls a slice of stored traces, weighted toward errors, flagged responses, and outliers, and a judge scores each one, which is only possible because the trace preserved the rendered prompt and retrieved context the model saw. The score lands back on the trace id, so a quality regression trends on dashboards, alerts like latency does, and every alert resolves to the exact traces that caused it.

LLM Observability and Tracing: Seeing Inside a Nondeterministic System

Name: System Design Masterclass
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