Databricks Certified Generative AI Engineer Associate — Question 43

A Generative AI Engineer has been asked to design an LLM-based application that accomplishes the following business objective: answer employee HR questions using HR PDF documentation.
Which set of high level tasks should the Generative AI Engineer's system perform?

Answer options

• A. Calculate averaged embeddings for each HR document, compare embeddings to user query to find the best document. Pass the best document with the user query into an LLM with a large context window to generate a response to the employee.
• B. Use an LLM to summarize HR documentation. Provide summaries of documentation and user query into an LLM with a large context window to generate a response to the user.
• C. Create an interaction matrix of historical employee questions and HR documentation. Use ALS to factorize the matrix and create embeddings. Calculate the embeddings of new queries and use them to find the best HR documentation. Use an LLM to generate a response to the employee question based upon the documentation retrieved.
• D. Split HR documentation into chunks and embed into a vector store. Use the employee question to retrieve best matched chunks of documentation, and use the LLM to generate a response to the employee based upon the documentation retrieved.

Correct answer: D

Explanation

The correct answer, D, effectively breaks down HR documentation into manageable chunks, allowing for precise retrieval based on the employee's question, which is essential for generating accurate responses. Option A focuses on averaged embeddings, which may not represent the specific needs of the query. Option B summarizes documents but does not ensure the information is relevant to the specific question asked. Option C involves a more complex interaction matrix approach, which may not be as efficient for real-time querying as the chunk retrieval method in D.