AI FUNDAMENTALS

RAG (Retrieval-Augmented Generation)

AI Fundamentals

Definition

RAG is a technique that combines information retrieval from a document base with AI text generation. Instead of relying solely on the model's knowledge, the system first searches for relevant documents (manuals, FAQs, contracts) and then generates responses based on those documents. It is the most effective approach for creating reliable business chatbots.

Related terms

AI Fundamentals

Embedding

An embedding is a numerical representation (vector) of text, an image, or other data that captures semantic meaning. Texts with similar meaning have embeddings that are close in vector space. This technique is fundamental for semantic search, recommendation systems, and RAG, enabling information retrieval by meaning rather than just keywords.

AI Fundamentals

Vector

In AI, a vector is an ordered list of numbers that represents a concept, document, or data point. Vector databases (such as Pinecone or Weaviate) efficiently store and search these vectors, allowing you to find content that is similar in meaning. They are at the heart of modern RAG systems and enterprise semantic search applications.

NLP & Language

Knowledge Graph

A knowledge graph is a data structure that represents entities (people, products, concepts) and the relationships between them in graph form. Google uses it to enrich search results. In business, a knowledge graph can link customers, products, orders, and support tickets, allowing AI to navigate enterprise knowledge in a structured way.

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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