AI Engineering Career Path
10 modules on building with foundation models: evaluation, prompting, RAG, agents, fine-tuning, inference and system design. Short lessons, flashcards, practice questions, scenarios and labs.
- Beginner to intermediate
- 10 modules
- Modules 1 and 2 free
Modules
Module 1 of 10 · about 1.5 h
AI Engineering Foundations
Distinguish foundation-model application work from model training; select appropriate use cases.
Module 2 of 10 · about 1.5 h
Language Models and Generation
Explain tokenization, model outputs, context and sampling choices.
Module 3 of 10 · about 1.5 h
Evaluation Methodology
Design measurements and judge output quality against explicit criteria.
Module 4 of 10 · about 1.5 h
System Evaluation and Reliability
Test end-to-end systems for robustness, safety, drift, and user impact.
Module 5 of 10 · about 1.5 h
Prompt Design and Security
Write clear prompts, compare variants, and defend against untrusted input.
Module 6 of 10 · about 1.5 h
Retrieval-Augmented Generation
Plan indexing, retrieval, context assembly, and citation quality.
Module 7 of 10 · about 1.5 h
Agents and Tool Use
Implement bounded tool use with approval, recovery, and audit trails.
Module 8 of 10 · about 1.5 h
Fine-tuning and Data Quality
Determine when adaptation is justified and prepare trustworthy training data.
Module 9 of 10 · about 1.5 h
Inference Cost and Performance
Balance model quality, request costs, serving latency, and resilience.
Module 10 of 10 · about 1.5 h
Production Architecture and Feedback
Ship measurable AI experiences with logging, safety, and improvement cycles.