That state-of-the-art LLM embedding may not work for your use case — here is why
With LLM embedding-based applications, it’s quite easy to understand and apply semantic meaning in machine learning algorithms. For example, you can use text embedding to feed to a classifier for rating user sentiment on user feedback or use it for RAG-based semantic search, etc. We understand words by mapping them in different dimensions. For example, relative terms such as good, better and best or low carb vs high carb food. If we represent the word by rating these values in...
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