Databricks Generative AI Engineer Associate Question 305
Select 4You are designing a Retrieval-Augmented Generation (RAG) application on Databricks to integrate with a Large Language Model (LLM). The application needs to minimize costs while maintaining performance. Which of the following approaches can help optimize LLM usage costs in Databricks?
- A
Implement caching for frequently used query results.
- B
Use Databricks' Auto Loader to directly query the LLM for each request.
- C
Fine-tune the LLM on your specific dataset using Databricks' MLflow integration.
- D
Leverage vector databases to retrieve relevant context and reduce token input size.
- E
Set up usage logging and monitoring to identify and optimize high-cost queries.
Show answer and explanation
Correct answers: A, C, D, E
Explanation
Optimizing LLM costs in Databricks for a RAG application involves strategies like caching frequently used results, fine-tuning models for targeted accuracy, and leveraging vector databases to reduce the token input size. Additionally, usage monitoring helps identify and address inefficiencies. Directly querying the LLM for every request without optimization is not cost-effective.
- A. Correct.
Caching frequently used query results reduces the number of calls to the LLM, lowering costs while speeding up response times.
- B. Incorrect.
Querying the LLM for every request without optimization increases costs unnecessarily and is not a cost-effective solution.
- C. Correct.
Fine-tuning the LLM on your dataset can improve its accuracy for your use case, reducing the need for excessive token usage or redundant queries.
- D. Correct.
Using vector databases allows the system to efficiently retrieve relevant context, reducing the token input size sent to the LLM, which directly reduces costs.
- E. Correct.
Setting up usage logging and monitoring enables you to identify high-cost queries and optimize them for cost efficiency.