Databricks Generative AI Engineer Associate Question 19
Select 3You are designing a Generative AI pipeline using a large language model (LLM) in Databricks. The pipeline needs to take user-provided text queries as input, retrieve relevant documents from a database, and generate a summarized response from the retrieved documents. Which chain components should you include to achieve this functionality?
- A
Retrieval-based Question Answering Chain
- B
Summarization Chain
- C
Embedding Generation Chain
- D
Text-to-Image Generation Chain
- E
Prompt Template Chain
Show answer and explanation
Correct answers: A, B, E
Explanation
To construct the pipeline, you need components that handle the retrieval of relevant documents (Retrieval-based Question Answering Chain), generate a summarized output (Summarization Chain), and correctly format and structure inputs for the model (Prompt Template Chain). These components together fulfill the end-to-end functionality described in the scenario. Other options like Embedding Generation Chain and Text-to-Image Generation Chain are either unrelated or unnecessary for this specific use case.
- A. Correct.
The Retrieval-based Question Answering Chain is essential for fetching relevant documents from a database based on user input, which matches the requirement to retrieve information.
- B. Correct.
The Summarization Chain is needed to condense and generate a summarized response from the retrieved documents, which aligns with the desired output.
- C. Incorrect.
While embedding generation is useful for vector similarity tasks, it is not directly required for this pipeline unless specified. The question does not indicate a need for embeddings.
- D. Incorrect.
Text-to-Image Generation Chain is irrelevant to the scenario since the pipeline is focused on text-based input and output, not image generation.
- E. Correct.
The Prompt Template Chain helps structure and format the input to the LLM appropriately, ensuring that the model performs the desired task effectively. This is crucial for aligning the input with the required textual response.