AI FUNDAMENTALS

Few-Shot Learning

AI Fundamentals

Definition

Few-shot learning is a technique in which you provide the AI model with a few examples of the desired task directly in the prompt. For instance, by showing 3 emails classified as 'urgent' or 'normal', the model learns the pattern and classifies subsequent emails. It is a quick and practical way to customize AI without the need for fine-tuning.

Related terms

AI Fundamentals

Zero-Shot Learning

Zero-shot learning is the ability of an AI model to perform a task without ever having seen specific examples of that task. Only a text description of what to do is provided. Modern models like Claude and GPT-4 excel at zero-shot, making it possible to use AI for new business tasks simply by describing what is needed.

NLP & Language

Prompt Engineering

Prompt engineering is the art and science of writing effective instructions to get the best results from AI models. It includes techniques such as few-shot learning, chain-of-thought, system roles, and output structuring. It is a key skill for any company using AI: a good prompt can make the difference between a useless and an excellent output.

AI Fundamentals

Chain-of-Thought

Chain-of-thought is a prompt engineering technique that guides the AI model to reason step by step before providing an answer. By explicitly asking 'reason step by step', you get more accurate responses on complex problems such as financial analyses, calculations, and strategic decisions. It is particularly useful for business tasks requiring logical reasoning.

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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