Large Language Models: Build, Fine-Tune and Deploy LLMs

Go beyond prompts and build modern LLM systems

Step into the front lines of artificial intelligence with Large Language Models, the technology driving today’s smartest tools and applications.

If you want to move beyond basic API calls and truly understand how LLMs work, adapt, and process information under the hood, this course provides the technical bridge from foundational NLP to cutting-edge AI engineering.

Learn by building, not just reading

Designed for engineers and data enthusiasts ready to dive deep, this course skips high-level fluff and drops you directly into code. You’ll work through short, focused modules using industry-standard tools, vector databases, and modern frameworks.

Starting with NLP foundations, you’ll work with tokenisation, embeddings, classifiers, and language modelling before progressing to neural networks and modern transformer architectures.

Build and fine-tune modern transformers

Build key transformer components from scratch, including Rotary Position Embeddings (RoPE) and Llama-style layers, before applying prompt engineering, Parameter-Efficient Fine-Tuning (PEFT), and LoRA to adapt pre-trained models.

Construct RAG and multimodal AI applications

Build end-to-end Retrieval-Augmented Generation (RAG) pipelines using vector storage and knowledge-grounded semantic inference. Finally, create GenAnkiCards, combining LLM text generation with dynamic image and audio components and Anki integration.

By the end, you’ll be able to understand LLM internals, build transformer models, fine-tune efficiently, construct production RAG pipelines, and develop full-stack AI applications.

This course is for software developers, data analysts, computer science students, and technical professionals with basic Python knowledge who want to understand how LLMs are built, fine-tuned, and applied in modern AI systems.

This course is for software developers, data analysts, computer science students, and technical professionals with basic Python knowledge who want to understand how LLMs are built, fine-tuned, and applied in modern AI systems.

• Apply NLP and language modelling techniques to process text, build models, and evaluate their performance.
• Experiment with core components of transformer-based language models, including attention, positional embeddings, and transformer layers.
• Apply prompt engineering and parameter-efficient fine-tuning techniques to adapt foundation models for specialised tasks.
• Design and implement Retrieval-Augmented Generation pipelines using embeddings, vector stores, and retrieved context.
• Develop practical LLM-powered applications that integrate language models with external tools, APIs, and services.