{"id":5805,"date":"2026-11-13T14:25:15","date_gmt":"2026-11-13T17:25:15","guid":{"rendered":"https:\/\/nextage.com.br\/blog\/?p=5805"},"modified":"2026-11-13T14:25:15","modified_gmt":"2026-11-13T17:25:15","slug":"rag-llm-and-generative-ai-explained-for-decision-makers-what-each-one-does-and-what-it-costs","status":"publish","type":"post","link":"https:\/\/nextage.com.br\/blog\/en\/rag-llm-and-generative-ai-explained-for-decision-makers-what-each-one-does-and-what-it-costs\/","title":{"rendered":"RAG, LLM and Generative AI Explained for Decision-Makers: What Each One Does and What It Costs"},"content":{"rendered":"

Updated on November 13, 2026. Model prices change within weeks; every figure below carries a source and a date.<\/em><\/p>\n

RAG, LLM and generative AI show up together in almost every enterprise AI proposal, and they are often treated as synonyms. They are not. Generative AI is the category; the LLM (large language model) is the engine that writes the answers; RAG is the technique that looks up company data before the model responds. Each layer has a different cost, and the most expensive one is rarely the model.<\/p>\n

This guide is for CTOs, CIOs, and technology leaders who need to explain the topic to the board or evaluate vendor proposals. It defines each term, shows how they connect, and lays out the math, with dated prices, stated assumptions, and sources. The market already moves large sums: Gartner<\/a> forecasts US$ 28.3 billion in spending on generative AI models in 2026, up 117% from 2025 (a spending forecast, not a measured result).<\/p>\n

\"Hands<\/p>\n

What is generative AI?<\/h2>\n

Generative AI is the category of artificial intelligence that creates new content (text, code, images, audio) from patterns learned in training data. McKinsey<\/a> describes it as algorithms, such as ChatGPT, that can create new content; in text models, training is self-supervised: the system reads large volumes of text and learns to predict what comes next. (The McKinsey page linked here is the Portuguese edition.)<\/p>\n

In companies, typical uses include summarizing documents, drafting text, generating code, and answering questions (illustrative examples, not a survey). Two caveats apply to any decision: not all generative AI works with text (there are image and audio models), and producing plausible content is not the same as producing correct content.<\/p>\n

What is an LLM?<\/h2>\n

An LLM (Large Language Model) is an AI model trained on large volumes of text to predict and generate language; it is the engine behind chatbots and text assistants. According to Fast Company Brasil<\/a>, the technical basis is the transformer architecture, introduced in 2017; text is split into tokens (words, word fragments, or characters), and at each step the model calculates the most likely next term.<\/p>\n

For decision-makers, three limits matter:<\/p>\n