Vector databases store data as vector embeddings for efficient retrieval of semantically similar data, crucial for AI applications like OpenAI's GPT-4.
Vector search reduces 'hallucinations' in LLM applications by providing additional relevant information not present in the training dataset, improving AI understanding.
Vector search minimizes the need for excessive retraining and fine-tuning in AI/ML applications, enhancing efficiency in development and deployment.
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