Databricks Generative AI Engineer Associate Question 20
Select 3You are designing a generative AI application on Databricks that takes a natural language question as input and returns a summarized answer. You plan to use a pre-trained language model for this purpose. Which chain components should you select to ensure the input is properly processed and the output is correctly generated?
- A
A PromptTemplate to structure the input question before passing it to the model
- B
A Tokenizer to convert the input question into a numerical format compatible with the model
- C
A Memory component to store the conversation history for context
- D
A Vector Database to retrieve relevant documents for context
- E
An Output Parser to convert the model's raw output into a human-readable summarized answer
Show answer and explanation
Correct answers: A, B, E
Explanation
To successfully process natural language input and generate a summarized output, you need specific chain components. The PromptTemplate ensures the input is well-structured for the model. The Tokenizer converts the input into a format the model can process. Finally, the Output Parser refines the model's raw output into a user-friendly summarized response. Memory and Vector Databases are not relevant for this specific task as there is no conversational context or document retrieval involved.
- A. Correct.
A PromptTemplate is essential for structuring and formatting the input in a way that aligns with the model's expectations, improving the quality of the output.
- B. Correct.
A Tokenizer is required to preprocess the natural language input into a numerical format that the model can interpret.
- C. Incorrect.
A Memory component is useful in conversational AI to track history but is not necessary in this scenario, as the task involves a single input-output pair.
- D. Incorrect.
A Vector Database is used in retrieval-augmented generation workflows. However, it is not required here as the task does not involve retrieving external documents.
- E. Correct.
An Output Parser is needed to convert the raw output of the model into a properly summarized and human-readable format.