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  • Getting Started
    • Introduction
    • Sign up to Developer Edition
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  • Core Concepts
    • Agent
      • Knowledge
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    • PII Masking
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      • Node
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        • Javascript
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        • Vector Search
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    • RAG
    • Model Hub
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    • Code Snippets
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  • Overview of GenAI
    • Introduction
    • Key concepts
      • Intent Classification
      • Inference
      • Generative AI Models
      • Large Language Models (LLMs)
      • Prompt Engineering
      • AI Agents
      • RAG (Retrieval Augmented Generation)
      • AI Workflow Automation
      • AI Agents vs LLM-based APPs
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  1. Overview of GenAI
  2. Key concepts

Generative AI Models

Generative AI Models

Generative AI Models

A Generative AI Model is an advanced artificial intelligence (AI) model designed to process and generate human-like text based on vast amounts of data. It is trained using deep learning techniques, particularly transformer architectures (like GPT, BERT, or LLaMA), and can understand, predict, and generate language in a way that mimics human communication.

What It Is?

Generative AI models are algorithms that generate new content based on patterns they have learned from data. Instead of just analyzing or classifying data, they create text, images, music, or even code.

How Does It Work?

Think of it like a student learning to write essays by reading thousands of articles. Over time, the student can write original essays that sound natural. Generative AI does the same but much faster.

Examples:

  • GPT-4 (by OpenAI): Writes text, answers questions, helps with coding.

  • DALL·E 3: Creates images from text descriptions.

  • Stable Diffusion: Generates art and graphics based on inputs.

Where It’s Used?

  • Writing emails, blogs, and reports.

  • Generating financial summaries.

  • Creating marketing images and designs.

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Last updated 4 months ago