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Question 29/38

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:

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Question List (38q)
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Question 2: Which statement accurately reflects the differences between ...
Question 3: How are prompt templates typically designed for language mod...
Question 4: What is the purpose of Retrieval Augmented Generation (RAG) ...
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Question 6: What is the characteristic of T-Few fine-tuning for Large La...
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Question 19: What do embeddings in Large Language Models (LLMs) represent...
Question 20: How do Dot Product and Cosine Distance differ in their appli...
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Question 22: What do prompt templates use for templating in language mode...
Question 23: What is the role of temperature in the decoding process of a...
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Question 25: Which is NOT a built-in memory type in LangChain?...
Question 26: Which statement is true about string prompt templates and th...
Question 27: How does the integration of a vector database into Retrieval...
Question 28: Which is a key advantage of using T-Few over Vanilla fine-tu...
Question 29: Given the following prompts used with a Large Language Model...
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Question 32: How are fine-tuned customer models stored to enable strong d...
Question 33: What issue might arise from using small datasets with the Va...
Question 34: Which statement is true about the "Top p" parameter of the O...
Question 35: Which is a distinctive feature of GPUs in Dedicated AI Clust...
Question 36: Which is the main characteristic of greedy decoding in the c...
Question 37: Which component of Retrieval-Augmented Generation (RAG) eval...
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