Foundation Models · Advanced
LLM Engineering
The applied discipline of building products on large language models: grounding, adaptation, evaluation, safety and serving economics.
- Level
- Advanced
- Mathematics depth
- Essential
- Engineering depth
- Systems
- Modality
- Cohort-based
- Duration
- Announced per cohort
- Status
- Open for inquiry
Audience
Who this is for
- Engineers shipping LLM features who need depth beyond prompting
- Platform engineers building internal AI capabilities
- Technical founders validating LLM products
Career paths
Prerequisites
- — Production software experience
- — Familiarity with LLM APIs
- — Basic grounding concepts
Outcomes
Skills acquired
- Design LLM systems whose quality is measured, gated and monitored
- Choose between prompting, grounding and fine-tuning with evidence
- Control serving cost without sacrificing the quality bar
Tools used
Curriculum Architecture
Module progression
Expandable, visual, ordered. Each module is a prerequisite-aware step, not an isolated video.
- 01LLM Capability & Failure Taxonomy
- 02Grounding & Context Architecture
- 03Context & Memory Management
- 04Structured Outputs & Tool Use
- 05Adaptation: When and How to Fine-Tune
- 06Evaluation Harnesses & Regression Gates
- 07Safety, Policy & Output Verification
- 08Serving Economics: Caching, Routing, Tiering
- 09Observability & Drift
Projects
- A grounded assistant with citations and an eval gate
- A cost/latency analysis of routing strategies on a real workload
Assessment philosophy
Assessment is engineering review: written error analyses, measured system behavior, defended design decisions. We evaluate whether you can explain and justify what you built — because production will.
Stack position: Foundation Models → Systems.
FAQ
Frequently asked questions
Do I need a mathematics background?
It depends on the program. Foundation-tier programs start from the mathematics itself; advanced tiers list working linear algebra as a prerequisite. The Mathematics for AI program exists precisely to close that gap.
Is this a bootcamp?
No. CAS Studies is an engineering institute. Programs are built around architectures, derivations and projects with written error analysis — not tutorial replays.
How long does a program take?
The two diploma tracks run on fixed lengths — AI Architectural Engineering spans 18 months, AI Application Engineering spans 1 year. All other program durations are announced per cohort, by modality.
Will I build real systems?
Yes. Every program ends in projects that resemble production work: evaluated models, grounded answer systems, supervised agents — with measurement, not vibes.