DSA-C03 Exam Dumps | You are using Snowflake Cortex to build a customer support chatbot that leverages LLMs to answer customer

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Question 33/143

You are using Snowflake Cortex to build a customer support chatbot that leverages LLMs to answer customer questions. You have a knowledge base stored in a Snowflake table. The following options describe different methods for using this knowledge base in conjunction with the LLM to generate responses. Which of the following approaches will likely result in the MOST accurate, relevant, and cost-effective responses from the LLM?

A. Directly prompt the LLM with the entire knowledge base content for each customer question. Concatenate all knowledge base entries into a single string and include it in the prompt.

B. Use Snowflake Cortex's 'COMPLETE function without any external knowledge base. Rely solely on the LLM's pre-trained knowledge.

C. Use Retrieval-Augmented Generation (RAG). Generate vector embeddings for the knowledge base entries, perform a similarity search to find the most relevant entries for each customer question, and include those entries in the prompt.

D. Fine-tune the LLM on the entire knowledge base. Train a custom LLM model specifically on the knowledge base data.

E. Partition your database by different subject matter and then query the specific partitions for your information.

Correct Answer: C

RAG (Retrieval-Augmented Generation) is the most effective approach (C). It combines the benefits of LLMs with the ability to incorporate external knowledge. Prompting with the entire knowledge base (A) is inefficient and might exceed context limits. Relying solely on the pre-trained LLM (B) won't leverage your specific knowledge base. Fine-tuning (D) is expensive and requires significant effort and only parititioning (E) won't help.

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