From the course: LLM Foundations: Vector Databases for Caching and Retrieval Augmented Generation (RAG)
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LLMs as a knowledge source
From the course: LLM Foundations: Vector Databases for Caching and Retrieval Augmented Generation (RAG)
LLMs as a knowledge source
As LLMs have become popular over the last many months, they are used for multiple purposes. This is because of their wide range of capabilities. There are two main capabilities of LLMs. The first is the language capability. LLMs can understand text and that too in several languages. They can learn the semantics and meaning from the text for reasoning. They can also generate human sounding text as responses. They can translate text from one language to another. The second is the knowledge capability. Given that LLMs are trained on a wide corpus of data, they can also answer questions related to their training data. They can help with distilling knowledge from these training sources and provide concise answers. There is hence the initiative to use LLMs as a source of knowledge. But this approach has a few shortcomings. First, LLMs can only answer questions based on the data they are trained on. LLMs are usually trained on public data that is available on the Internet. They are only good…
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