NemoClaw Knowledge Wiki

generative-ai-models

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🗂️ AI & Agents · View mindmap

Generative AI Models

Generative AI models are neural networks trained to produce new content—text, images, code, or other data—based on patterns learned from training data. These models operate by predicting the next token (word, character, or data unit) in a sequence, repeating this process iteratively to generate coherent outputs. Modern generative models power numerous applications across software, creative industries, research, and business analytics.

Context Windows and Model Capability

A critical factor in generative model performance is the context window—the amount of input text a model can process at once. Larger context windows enable models to maintain consistency across longer documents, understand complex relationships between distant information, and produce more contextually appropriate responses. Early language models operated with limited context windows (typically 2,000-4,000 tokens), while contemporary models support windows of 100,000 tokens or more, significantly expanding their practical applications.

Agentic RAG Systems

Retrieval-Augmented Generation (RAG) systems represent an important maturation in generative AI deployment. RAG systems combine generative models with external knowledge retrieval, allowing them to ground responses in specific documents or databases rather than relying solely on training data. Agentic RAG systems extend this further by enabling the model to autonomously decide when to retrieve information, what queries to perform, and how to iteratively refine results—essentially delegating search and reasoning tasks to the model itself. This approach addresses limitations in hallucination and knowledge currency, making generative models more reliable for applications requiring factual accuracy and up-to-date information.

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