[100% Off] Llm Production Engineering: Ship Ai Apps That Scale
Serving, production RAG, evals, observability, guardrails and cost control — measured, built, ready for real users.
What you’ll learn
- Map any LLM product against the four production forces — latency
- cost
- reliability
- drift — and find its real risks,Choose hosted APIs vs self-hosting with token math and utilization
- not fashion,Run prompts as code: versioned
- reviewed
- with structured outputs and safe change gates,Engineer latency: streaming
- caching
- context budgets — and read the tail
- not the average,Measure cost to the token with a real tokenizer
- then cut it with compression
- caching and cascades behind a quality gate,Build reliability ladders: timeouts
- bounded retries
- fallback tiers
- circuit breakers,Design
- build and gate a production RAG service — contracts
- census
- citations
- abstention
- evals that can block a deploy,Instrument LLM systems with structured traces and a completeness contract; redact PII before it reaches a log,Defend the stack: injection batteries
- corpus allowlists
- citation-bound assembly — measured
- not hoped,Answer LLM system-design interviews with a five-move framework and live arithmetic
Requirements
- You can call an LLM API (or any hosted model endpoint) from Python and read the response — this course starts where that skill ends,Working Python (functions
- classes
- JSON
- basic testing) — the projects are standard-library-first and every listing is run on screen,No GPU
- no paid API keys and no cloud account required: every project system runs locally and deterministically
Description
This course contains the use of artificial intelligence.
Your LLM demo works. Your production system is where it goes to die — in the latency tail your users feel, in a bill nobody measured, in an eval set that drifted away from real traffic, in an injection attack a user pastes in on a Friday. This course is the engineering between the demo and the system that survives: serving and inference, prompts as code, latency and cost engineering, reliability ladders, context budgets, evaluation with golden sets and CI gates, observability, and guardrails — every topic taught as a measurement or a build, never as a slideware claim.
The core ten lectures map the whole discipline: the four forces that kill LLM products (latency, cost, reliability, drift); hosted APIs versus self-hosting, decided with numbers; prompts under version control with structured outputs; latency engineering — streaming, caching, and the tails that matter more than the average; token economics and cascades; retries, fallbacks and circuit breakers; context budgets, memory and fresh RAG; evaluation with golden sets, judges and CI gates; traces and injection defence; and the deployment architecture that assembles it all.
Then you build three production systems. Project 1: AnswerDesk, a RAG service over a real corpus — designed by contract and census, built with citation-bound answers and abstention guards that took three measured calibration rounds to get honest, and shipped behind an evals gate that can block its own deploy. Project 2: the Optimization Lab — a 240-request workload measured to the token with a real tokenizer, then compressed, cached and cascaded from $479.66 to $25.91 a month (94.6% down) behind a quality gate that had to pass before the savings counted. Project 3: ShipGate — structured traces for every request, a 12-attack injection battery refused 12/12 with zero false refusals, PII masked before it ever reaches a log, and a five-gate ship board you will watch fail three different ways on purpose.
Then you crack the interview. Four system-design lectures with the arithmetic done live: a chatbot that survives launch day (40 requests/second becomes $1,656 per peak hour — then gets rescued, honestly), RAG at ten million documents (twenty billion tokens become five hundred gigabytes of vectors — then get designed down), cutting inference cost 10× with measured lever factors, and a five-move playbook with a ten-question rapid-fire drawn from the whole course.
Every number in this course was produced by running the system on screen — census counts, ranking scores, gate boards, cost figures. Nothing is asserted from memory, and where a board is a fixture’s property rather than a law of nature, the lecture says so. You also get downloadable code and exercise books, six checkpoint quizzes with a 70% mastery rule, five hands-on assignments with worked solutions, and a capstone: ship your own production LLM service behind your own ship gate.
AI disclosure: this course’s narration uses a cloned voice of the instructor, created with the instructor’s consent; all content, code, and curriculum are authored and verified by the instructor team. Music: “Frozen Star” by Kevin MacLeod — CC BY 4.0.
