{"id":18976,"date":"2025-06-10T13:01:47","date_gmt":"2025-06-10T16:01:47","guid":{"rendered":"https:\/\/loxias.ai\/?p=18976"},"modified":"2026-10-23T15:30:15","modified_gmt":"2026-10-23T14:30:15","slug":"rag-the-secret-behind-ais-to-business","status":"publish","type":"post","link":"https:\/\/loxias.ai\/es\/blog\/rag-the-secret-behind-ais-to-business\/","title":{"rendered":"RAG: How AI Understands Your Business Better"},"content":{"rendered":"\n

Imagine asking an AI assistant a question, and it responds with up-to-date information from a report that only you have. Sounds like magic? It\u2019s not\u2014this is RAG in action. RAG, or Retrieval-Augmented Generation, is a technique that allows AI models to query custom data sources\u2014such as company-specific documents like PDFs, spreadsheets, databases, or even APIs\u2014to generate more accurate and up-to-date answers.<\/span><\/p>\n\n\n\n

In this article, you\u2019ll learn in simple terms how this works and how technologies like LlamaIndex, LangChain, and CrewAI are making this idea a reality that has gone far beyond the hype.<\/span><\/p>\n\n\n\n

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Why do we need RAG?<\/b><\/h4>\n\n\n\n

Large Language Models (the famous LLMs, such as GPT-4<\/a>, Gemini<\/a>, Claude, DeepSeek<\/a>, among others) already know how to answer a multitude of questions, but there is a limit: they only know what was included in their training and in the free versions up to a certain date. This means that a traditional LLM cannot answer based on your company\u2019s internal documents or the most recent data. This is where RAG comes in: it allows the model to search for the information you need, at the moment you ask.<\/span><\/p>\n\n\n\n

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How does RAG work?<\/b><\/h5>\n\n\n\n

The process is simpler than it seems. Here\u2019s how it works:<\/span><\/p>\n\n\n\n

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  1. Data is loaded<\/b> documents, PDFs, spreadsheets, websites, databases, and APIs, whatever you need.<\/span><\/li>\n\n\n\n
  2. It\u2019s organized and indexed<\/b>, which makes it easier for the AI \u200b\u200bto search for information more quickly.<\/span><\/li>\n\n\n\n
  3. When you ask a question, the system consults the index and retrieves the most relevant excerpts<\/b>; sends this information along with your question to the AI \u200b\u200bmodel; generates a personalized answer, using both what the model already knows and what it just consulted.<\/span><\/li>\n<\/ol>\n\n\n\n

    Figure 1 demonstrates this entire process in a very didactic way.<\/span><\/p>\n\n\n\n

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    Source: Cognitiveclass.ai<\/figcaption><\/figure>\n\n\n\n

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    This approach is especially useful for:<\/span><\/p>\n\n\n\n