Generative AI

Past the prompt demo, into the shipped product

For developers who can already prompt a model and now need to ship one. It concentrates on the engineering that separates a demo from a product — selecting and adapting models, wiring them to your own data through retrieval, working across images and audio, and measuring whether the result actually holds up.

What you will learn

→Select and cost models, and adapt them with parameter-efficient fine-tuning (PEFT)
→Build retrieval-augmented (RAG) systems backed by a vector database
→Evaluate generation quality with frameworks such as RAGAs
→Extend to multimodal apps over text, images and audio, with observability built in

Curriculum

1Generative AI fundamentals
2Large language models (LLMs) and retrieval-augmented generation (RAG)
3Multimodal AI applications

The gap in generative AI today is not writing a clever prompt — it is making a system that stays reliable once real users hit it. This program lives in that gap: large language models connected to your data through retrieval-augmented generation, prompt engineering you can depend on, and honest evaluation. It assumes the deep-learning and Transformer groundwork is already in place.

Prerequisites

Intermediate Python
Deep-learning foundations
Familiarity with PyTorch, Hugging Face and Transformers
Basic prompt engineering and database fundamentals

Concepts this course teaches

Generative AI
Production generative AI is the whole point.
Large Language Models
A full module on LLMs and RAG.
Retrieval-Augmented Generation
Builds end-to-end RAG on a vector database.
Prompt Engineering
Reliable prompting for production apps.
Transformers
Assumes and extends Transformer knowledge.
Deep Learning
Builds on a deep-learning foundation.

FAQ

What should I know before starting?: Comfortable Python plus a working grasp of deep learning and Transformers. This is not where you meet neural networks for the first time; it is where you operationalise them.
Does it cover RAG and fine-tuning specifically?: Yes — a full module builds RAG on a vector database, and you practise lightweight adaptation with PEFT rather than training models from scratch.