LLM RAG enhances traditional language models by integrating them with external knowledge bases, databases, and multimodal inputs like text, images, or audio. This integration allows for more accurate, context-aware outputs, making these models particularly valuable in specialized domains.
Multimodal and verticalized LLMs deliver context-rich responses tailored to specific industries. By incorporating external data, these models enhance accuracy, provide flexibility in handling diverse data types, and improve relevance and user experience across specialized applications.
LLM RAG models are well-suited for various industries. In healthcare, they aid in interpreting medical data; in finance, they enhance risk analysis and decision-making; in retail, they improve customer interactions by integrating product data and user preferences.
Explore how fine-tuned LLMs can revolutionize specialized tasks across various industries, delivering precision and tailored performance.
Gather, create, or curate prompt-generation for your Generative AI model
Ask Data experts to enhance model accuracy
Evaluate and rank prompts and results
Get human feedback to score and categorize responses
Conduct model evaluations to refine performance
Identify and remove negative generated content
Review prompts and outputs for potential issues, with adversarial testing
Provide real-time support for Generative AI models in production
Conduct ongoing human verification and confirmation for classifier support
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