TechnicalAI glossary
Fine-tuning
Lightly retrain an existing model on specific data to specialise it: your style, your profession, your documents.
In other words
A chef already trained who learns the recipes of YOUR home in just a few weeks.
Alyriaz articles that mention it
- Mid-training, final fine-tuning: what happens after pre-training
Between a base model and a usable model, there are several distinct training phases. We explain what each one does and why that is where the value now lies.
- Should we ask whether an AI has rights?
A group is campaigning for AI rights. We take a serious look at what such a question would entail, why it cannot be answered today, and what to do with that uncertainty.
- Why doesn't the same AI respond differently depending on the app?
The same model produces very different responses depending on the application using it. We explain the five factors behind this gap, and why it's useful to know.
- General-purpose or specialised model: which one should you choose for your job?
Faced with the vertical models emerging in law, healthcare or finance, we explain when a specialised model genuinely adds value and when a well-equipped generalist is enough.
- RAG or fine-tuning: which one should you choose to connect an AI to your data?
To adapt an AI to your data, two approaches are at odds: RAG and fine-tuning. We explain the difference, when to use one or the other, and why most projects pick the wrong method.
- How does an AI "learn"? A model's journey, from raw chaos to the polished assistant
How does a model go from a pile of raw data to an assistant capable of answering you politely? We describe the three main stages of AI training (pre-training, fine-tuning, RLHF) with a simple analogy, to understand where its strengths and biases come from.