Advanced AI - production-grade engineering.

Who this course is for

You've finished Intermediate AI or you're already shipping AI features and the prototypes are piling up. You want the patterns that keep agents from going sideways, evaluations you can defend in a review, and the operational levers that make AI costs predictable. This is where SPUR's own engineering playbook lives.

Prerequisites

• Required: Intermediate AI (or equivalent experience with RAG, model selection, and at least one LLM app in production).
• Comfort with Python and HTTP APIs.
• Familiarity with at least one vector store and one LLM provider's API.

What you'll learn

Ten modules, three lessons each. Every module ends with a quiz. Every architectural pattern ships with an inline diagram.

Module 1 - Agents

What an agent actually is, how the control loop works, and the failure modes you have to design against from day one.

Module 2 - Fine-tuning

When it's the right answer, when it's not, and how to do it without torching a GPU budget.

Module 3 - Evaluation

Evals that catch real regressions. Offline and online. Human-in-the-loop where it matters.

Module 4 - Structured outputs

JSON mode, function calling, schema enforcement, and recovering gracefully when the model still gets creative.

Module 5 - Multi-agent orchestration

Multiple specialised agents, coordination patterns, shared memory, and the part where you decide whether you actually need more than one.

Module 6 - Retrieval at scale

Hybrid search, reranking, sharding, and the cost curve that bites once you leave demo-size corpora.

Module 7 - Cost and latency

The real economics of serving AI. Where the money goes, where the milliseconds go, and the tradeoffs you can actually move.

Module 8 - Security

Prompt injection, data exfiltration, supply-chain risks, and the controls that a serious review will ask you to show.

Module 9 - Caching and batching

Prompt caching, response caching, batch inference - the unglamorous work that pays for itself in a week.

Module 10 - Production pipelines

End-to-end pipelines: data in, inference out, observability on every hop, rollback when things go wrong.

Format

• Cost: Free. No paywall.
• Pace: Self-paced - start anytime.
• Time estimate: Most learners finish in 6-10 weeks at ~4-5 hours/week.
• Assessments: Quizzes in every module.
• Diagrams: Inline mermaid diagrams for every major architecture pattern.
• Progress: Saved to your account, syncs across devices.

Certificate

Complete all ten modules at 100% and SPUR issues a signed, verifiable PDF certificate in your name. Free. The certificate lists the specific modules and is verifiable on spuric.com.

Ready to build production-grade AI?

Advanced AI is free. Signup takes 30 seconds. Start Module 1 immediately.