Retrieval Augmented Generation (RAG) is a popular, low-cost technique to boost GenAI response quality. But for many use cases, it still falls short.
What are entity extraction, entity disambiguation, and entity linking?
Automatic identification and resolution of entities within unstructured data sources is crucial to understanding and utilizing data for use in AI systems. Historically this has been difficult to do, and even harder to trust the results. Agolo’s hybrid, human-in-the-loop approach for discovering and compiling entity intelligence, ensures that its best-of-breed, entity graph technology delivers trustworthy, production grade outputs for mission-critical AI use cases.
How do you bring insights to unstructured data? An overview of how entity graphs build upon knowledge graphs to extract insights and improve information value.
Beating the current SOTA Knowledge Graph-to-Text (KG-to-Text) model on the WebNLG (Constrained) dataset with a fine-tuned Llama 2 7B Chat model.
Taking the first step toward a new brand
GenAI draws from your enterprise’s knowledge base, documentation, and training data to deliver responses. If the content is outdated, incomplete, or inconsistent, the AI may produce irrelevant or inaccurate outputs, eroding customer trust.
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