Lessons
1How LLMs actually behave42 min read
An LLM is not a function — same input, different output; finite memory you pay for by the token; latency that scales with what it writes. The generation loop, tokenization, the context window, sampling, reasoning models, and the interview questions that probe all of it.
- →LLM Behavior and Integration
- →Cost and Latency Control
- →Reliable LLM Service Delivery
2Prompting & context engineering48 min read
Prompting is API design for a probabilistic system — the static contract, the per-call context you assemble, decoding-time techniques (CoT, self-consistency, decomposition), and treating prompts as versioned, eval-gated artifacts. With the interview round that probes all of it.
- →Prompt and Context Design
3Structured outputs & function schemas48 min read
The moment code consumes the output, free-form text is a liability. Constrained decoding, strict schemas, function/tool calls, validation + repair, the schema-design choices that change accuracy — the failure modes that still bite at scale, and the interview round on all of it.
- →Structured and Grounded Output
4Building a reliable LLM client50 min read
The provider will time out, rate-limit, 500, and occasionally go down. The four-layer reliability stack — timeouts, classified retries with jitter, fallback, circuit breaker — plus idempotency, hedging, the gateway, and the interview round on resilient distributed clients.
- →LLM Behavior and Integration
- →Reliable LLM Service Delivery
5Cost & latency engineering48 min read
Latency is UX and cost is the bill — both are engineered. SLOs by use case, the prefill/decode latency model, streaming as perception, and the full lever stack: prompt caching, semantic caching, batching, routing/cascades, small fine-tunes — plus the FinOps interview round.
- →Cost and Latency Control
6Capstone: a summarize-and-extract microservice52 min read
Assemble the whole track — prompt contract + structured output + reliable client + caching + tests + observability — into a service that turns a document into a structured summary you can trust in production, and that you can defend end-to-end in a system-design interview.
- →Reliable LLM Service Delivery
- →Structured and Grounded Output
- →LLM Behavior and Integration
Skills in this course
- 01LLM Behavior and IntegrationExplain model variation, token generation, and context limits. Select an API pattern that fits the task.
- 02Prompt and Context DesignBuild small, versioned prompts. Separate instructions from dynamic and untrusted context.
- 03Structured and Grounded OutputCreate schemas and validators that give downstream code valid, grounded data and allow unknown values.
- 04Cost and Latency ControlEstimate token cost and latency. Use output limits, caching, routing, and model choice to meet service targets.
- 05Reliable LLM Service DeliveryBuild a service with safe retries, rate control, fallback checks, deterministic tests, eval gates, and request traces.