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topics/generative-ai/index.md

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+---
+aliases: gen-ai, genai, generative-artificial-intelligence
+display_name: Generative AI
+short_description: Generative AI refers to artificial intelligence models capable of generating text, images, code, or other media in response to prompts.
+topic: generative-ai
+wikipedia_url: https://en.wikipedia.org/wiki/Generative_artificial_intelligence
+---
+Generative AI (Generative Artificial Intelligence) encompasses machine learning models trained to create new content—including text, images, video, audio, and code—by learning patterns from existing data. Popular applications include large language models (LLMs), AI image generators, and autonomous coding agents.

topics/rag/index.md

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 ---
-aliases: retrieval-augmented-generation, rag-pipeline
-display_name: Retrieval-Augmented Generation
-short_description: Retrieval-augmented generation improves LLMs by retrieving relevant information from external knowledge sources.
+aliases: retrieval-augmented-generation, rag-application, rag-chatbot
+display_name: RAG
+short_description: RAG is a technique that combines information retrieval with AI language models to generate accurate, relevant, and context-aware responses.
 topic: rag
-logo: rag.png
 wikipedia_url: https://en.wikipedia.org/wiki/Retrieval-augmented_generation
-related: artificial-intelligence, generative-ai, llms, embeddings
 ---
-
-Retrieval-augmented generation (RAG) is a technique that improves large language models by retrieving relevant information from external sources and using it to generate more accurate and context-aware responses.
-
-A RAG system combines information retrieval with a language model. It is commonly used in AI assistants, search systems, document question answering, and applications that need access to private or frequently updated information.
+Retrieval-Augmented Generation (RAG) is an AI technique that enhances large language models (LLMs) by retrieving relevant information from external data sources. It combines information retrieval with language generation to provide accurate, relevant, and context-aware responses.