AI FUNDAMENTALS

Fine-Tuning

AI Fundamentals

Definition

Fine-tuning is the process of additional training of a pre-trained AI model on domain-specific data. It allows you to specialize a general model for particular tasks, such as classifying business documents or answering questions about your industry. It is more economical and faster than training a model from scratch.

Related terms

Machine Learning

Transfer Learning

Transfer learning is the technique of reusing a model trained on one task to solve another related task. Instead of starting from scratch, you start from a model that has already learned general patterns and adapt it to your specific case. It is the reason SMEs can achieve excellent AI results even with limited proprietary data.

AI Fundamentals

Training

Training is the phase in which an AI model learns from data, adjusting its parameters to improve performance on a specific task. It can require enormous computational resources and large amounts of data. SMEs rarely train models from scratch: they use pre-trained models via fine-tuning or prompt engineering, drastically reducing costs and time.

AI Fundamentals

Dataset

A dataset is a structured collection of data used to train, validate, or test an AI model. Dataset quality is directly proportional to the quality of results: incomplete, imbalanced, or erroneous data produce unreliable models. For SMEs, organizing and cleaning their business data is often the first step toward AI adoption.

EXPLORE

More terms in AI Fundamentals

Artificial Intelligence

Artificial intelligence is the computer science discipline that develops systems capable of performing tasks that normally require human intelligence: understanding language, recognizing images, making decisions. For Italian SMEs, AI represents a concrete opportunity to automate repetitive processes and achieve measurable competitive advantages.

Machine Learning

Machine learning is a subfield of AI in which systems automatically learn from data without being explicitly programmed for every scenario. ML algorithms analyze patterns in historical data to make predictions on new data. In business practice, it is used to forecast demand, classify documents, and optimize production processes.

Deep Learning

Deep learning is an advanced machine learning technique that uses neural networks with many layers (hence 'deep') to learn complex data representations. It underpins modern speech recognition, machine translation, and text generation systems. It requires large amounts of data but produces extraordinarily accurate results.

Neural Network

An artificial neural network is a computational model inspired by the workings of the human brain, composed of nodes (neurons) organized in interconnected layers. Each connection has a weight that is adjusted during training. Neural networks underpin nearly all modern AI systems, from image recognition to text generation.

Language Model

A language model is an AI system trained on enormous amounts of text to understand and generate natural language. Large language models (LLMs) such as GPT and Claude can write texts, answer questions, translate, and reason about complex problems. For businesses, they represent versatile assistants for any text-based task.

GPT

GPT (Generative Pre-trained Transformer) is the architecture behind ChatGPT, developed by OpenAI. It is a language model pre-trained on vast text corpora that can be used to generate content, answer questions, and perform complex text tasks. GPT-4 and subsequent versions have demonstrated increasingly advanced reasoning capabilities.

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