Given the following prompts used with a Large Language Model, classify each as employing the Chain - of-Thought, Least -to-Most, or Step -Back prompting technique: 1. Calculate the total number of wheels needed for 3 cars. Cars have 4 wheels each. Then, use the total number of wheels to determine how many sets of wheels we can buy with $200 if one set (4 wheels) costs $50. 2. Solve a complex math problem by first identifying the formula needed, and then solve a simpler version of the problem before tackling the full question. 3. To understand the impact of greenhouse gases on climate change, let's start by defining what greenhouse gases are. Next, well explore how they trap heat in the Earths atmosphere.
fine-tuning updates only parts of the model; it doesn’t replace the entire pre-trained weights, which remain largely intact. Which of the following is the MOST appropriate method for deploying an LLM application built with OCI Generative AI?
the prompt for context -aware generation. In the context of OCI Generative AI, what is the primary function of the Retriever component in Retrieval Augmented Generation (RAG)?
In the context of Retrieval -Augmented Generation (RAG) for LLM applications, how does a vector database contribute to the retrieval process?
During LLM fine -tuning, which layers of the model are typically adjusted the most?
Explanation: The self -attention mechanism helps the model understand how each word in a sequence relates to others, capturing context and meaning effectively. Option A is incorrect as self -attention is not used for image tasks in LLMs. Option B is a task that may use self-attention but not its primary function. Option D refers to embeddings, not self -attention. : Self-attention enables models to weigh word dependencies dynamically, improving contextual comprehension. What differentiates a code model from a standard LLM?
