Glossary term
Fine-Tuning
Adapting an already trained model to a specific task or domain by training it further on a smaller, focused dataset. It is cheaper than training a model from scratch.
Why it matters
Fine-Tuning sits at the centre of how modern AI systems are built and used, so understanding it changes how confidently you can work with the tools. In the Global South, where access to compute, devices and training is uneven, getting this vocabulary right is not academic — it is the difference between watching the AI shift and taking part in it.
In context
We use Fine-Tuning in the ai & machine learning part of our work. Students meet the concept in workshops, mentors reference it while reviewing projects, and partners see it in the reporting we send after every programme. Keeping the definition plain means a first-year student and a ministry official can hold the same conversation.
How Tembi Labs uses it
Fine-Tuning is not left on a slide. Our AI hackathons put concepts to work within hours: teams pick a local problem, use AI tools to prototype, and present something running by Sunday. Teams meet it directly the moment they open a model and start building — it stops being theory and becomes a setting they tune.
At a Tembi Labs hackathon, understanding Fine-Tuning means a student can move from asking "what is this?" to shipping a working prototype in a weekend — and walk away with a portfolio piece, a mentor network and a credible path into tech work.
Want to see Fine-Tuning applied on campus?
Book a call and we will walk you through how a Tembi Labs AI hackathon or computer room works at your university.