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Creating effective embeddings involves leveraging pre-trained models or training custom models tuned to specific domains, such as healthcare, finance, or e-commerce. Better domain training results in vectors that capture precise meanings relevant to specialized applications, reducing ambiguity and "hallucinations"—when the model generates incorrect or exaggerated information.
Types of Embeddings:
Word embeddings (e.g., Word2Vec, GloVe)
Sentence embeddings (e.g., SBERT)
Image embeddings
Audio embeddings
Code embeddings
Open-source options like Hugging Face models and OpenAI APIs simplify this process, allowing enterprises to efficiently generate embeddings for large datasets.