Generative AI Architecture Explained | RAG, Vector DB & LLM Guide

Q: What is generative AI in simple terms?

Generative AI is artificial intelligence that creates new content — text, images, code, audio — by learning patterns from training data. It does not copy; it generates original content by predicting what comes next based on patterns it has learned.

Q: How do LLMs store and retrieve knowledge?

LLMs store knowledge in billions of neural network parameters during training. At inference time, they retrieve knowledge by computing attention across these parameters for each input. External retrieval (RAG) supplements this with real-time data lookup.

Q: What is the difference between GPT, Claude, and Gemini?

GPT (OpenAI) leads in breadth and ecosystem. Claude (Anthropic) excels at long-context processing and safety. Gemini (Google) has the strongest multi-modal capabilities. All three are competitive for most enterprise use cases.

Q: Can generative AI replace human workers?

Generative AI augments rather than replaces most workers. It handles repetitive tasks (drafting, summarizing, classifying) while humans focus on strategy, creativity, judgment, and relationship-building. The most productive teams use AI as a force multiplier.

Satyam Kumar

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Generative AI Architecture

Generative AI Explained Simply — Storage, Retrieval, and LLM Architecture

February 11, 20264 min read

Retrieval Augmented Generation (RAG) AI System Design Vector Databases Large Language Models (LLM)

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Satyam

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Generative AI Explained Simply — Storage, Retrieval, and LLM Architecture

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