Large Language Models (LLMs) are capable of recognizing, summarizing, translating, predicting, and generating text. One of the most popular application of LLM is ChatGPT.

Several LLMs integrated into Upgini data search to improve the accuracy of ML models.

Upgini enriches input texts with contextual information from external data sources, instructs LLM based on a context, and LLM generate more accurate embeddings from a combination of initial text, contextual information and generated text.

Upgini automatically generates optimized embeddings using LLM's external data augmentation for text in both connected data sources and training datasets for search.

Just launch data search using Upgini and your labeled training dataset with text columns, and Upgini will generate LLMs embeddings from text columns and check it for predictive power for you ML task.

Finally Upgini will return you dataset enriched by relevant only components of LLMs embeddings.

Raw string from a training dataset:
The Nook

Description generated by LLM without augmentation from external data:
The Nook is a line of e-readers and tablets produced by Barnes & Noble...

Description generated by LLM with augmentation from external data sources and advanced instructions:
The Nook is a tattoo shop located in Jefferson City, Missouri. The shop is known for....