How does the integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models (LLMs) fundamentally alter their responses?
In the context of fine-tuning with OCI Generative AI Service, what is the role of a custom dataset?
Given the following code:
Prompt Template
(input_variable[‘’rhuman_input",'city’’], template-template)
Which statement is true about Promt Template in relation to input_variables?
What does the Loss metric indicate about a model's predictions?
What differentiates a code model from a standard LLM?
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