Databricks Generative AI Engineer Associate Question 206
Select 5You are tasked with creating a Retrieval-Augmented Generation (RAG) application on Databricks. Which of the following components are essential to build the RAG application successfully?
- A
Model flavor
- B
Embedding model
- C
Retriever
- D
Visualization tool
- E
Input examples
- F
Model signature
Show answer and explanation
Correct answers: A, B, C, E, F
Explanation
Building a Retrieval-Augmented Generation (RAG) application requires essential components such as a model flavor to define the type of model, an embedding model for vectorization, a retriever for fetching relevant documents, input examples for testing, and a model signature for defining the input-output schema. Visualization tools, while useful, are not mandatory for the functionality of a RAG pipeline.
- A. Correct.
Model flavor is necessary to specify the type of model being used, such as a large language model (LLM), which is key for the RAG application.
- B. Correct.
Embedding model is crucial to convert text data into vector representations, which are necessary for similarity searches in RAG pipelines.
- C. Correct.
Retriever is a core component of a RAG application as it fetches the most relevant documents or data from a given knowledge base.
- D. Incorrect.
Visualization tools are helpful for interpreting results but are not required to build a functional RAG application.
- E. Correct.
Input examples are important for testing and understanding the behavior of the RAG application under specific scenarios.
- F. Correct.
Model signature is required to define the input-output schema of the model, ensuring compatibility with the RAG pipeline.