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LLMs are transformer-based models trained on vast text corpora, enabling them to generate, summarize, and reason over natural language for a wide range of enterprise applications. [1] [2]

SLMs offer similar capabilities to LLMs but with reduced resource requirements, making them suitable for edge devices and cost-sensitive deployments. [1]

Prompt engineering is the iterative process of crafting and refining prompts to guide generative AI models toward producing accurate and relevant outputs for specific tasks. [1] [2]

RAG combines information retrieval with text generation, allowing LLMs to access up-to-date and domain-specific knowledge beyond their training data. [1] [2]

Vector databases store and index embeddings, enabling efficient similarity search and retrieval for tasks like semantic search and RAG implementations. [1] [2]

By integrating LLMs with enterprise data sources using RAG and vector databases, organizations can create intelligent, context-aware chatbots tailored to their needs. [1] [2]

Model adaptation and fine-tuning involve customizing a pretrained generative AI model on domain-specific data to improve its performance on targeted tasks. [1] [2]

Vision models like Stable Diffusion and DALL-E generate realistic images from prompts and support advanced editing techniques such as inpainting and outpainting. [1] [2]

Scalable deployment requires robust architecture, efficient data integration, cost management, and operational best practices to ensure reliability and performance. [1] [2]

Responsible AI practices include addressing risks like hallucinations and bias, implementing governance, and ensuring transparency and safety in model deployment. [1] [2]