AdvancedAI401

Building AI Applications with LLMs

For ML engineers who have deployed models and want to go beyond API integration. This course covers prompt architecture as a typed contract, three-layer output validation, eval frameworks, cost and latency optimisation, observability, and the governance obligations that come with systems that affect real decisions.

Lessons are AI-assisted and human-reviewed. Learn more.

Syllabus

LLM Behaviour as an Engineering Surface

The Probabilistic Contract: What LLMs Actually GuaranteeFree preview

Prompt Engineering as a Type System

Context Window Architecture: What Goes Where and Why

Validation Architecture for LLM Outputs

Output Validation Layers: Schema, Semantic, and Behavioural

Testing LLM-Powered Systems: The Evaluations Framework

Validating LLM-Generated Code in Data Pipelines

Application Architecture Patterns

Orchestration Patterns: When to Use Frameworks and When Not To

Cost, Latency, and Throughput: Engineering the LLM Budget

Observability and Failure Detection in LLM Systems

Professional Context — Responsibility, Risk, and Governance

Risk Tiering LLM Applications: What Can Go Wrong and at What Cost

Oversight Systems: Designing the Human-in-the-Loop Interface

Responsible AI in Production: Audit, Documentation, and Governance