Build, fine-tune and deploy your own AI models

A step-by-step guide to training a custom AI model on your own data: data readiness, fine-tuning vs. from-scratch, dataset preparation, evaluation, quantisation and deployment.

A practical comparison of fine-tuning and retrieval-augmented generation: what each changes, when each wins, and how to combine them for a domain-specific assistant.

Where the money actually goes when you train a custom AI model — data preparation, compute, evaluation and running costs — and how to keep each one down.

A practical cleaning checklist for fine-tuning datasets: deduplication, formatting, label checks, balanced splits and the review pass that catches errors.