RAG enables you to combine your vector store with the LLM itself. This combination allows the LLM to reason not just on its own pre-existing knowledge but also on the actual knowledge you provide through specific prompts.
RAG empowers organizations to harness the full potential of their data, providing a more efficient and accurate way to interact with AI-driven solutions.
Traditional LLMs are trained on vast datasets, often called 'world knowledge'. However, this generic training data is not always applicable to specific business contexts.
#generative-ai #large-language-models #retrieval-augmented-generation #business-applications #data-utilization
Collection
[
|
...
]