Databricks Certified Generative AI Engineer Associate Exam - Topic 2 Question 3 Discussion

Actual exam question for Databricks's Databricks Certified Generative AI Engineer Associate exam

Question #: 3
Topic #: 2

[All Databricks Certified Generative AI Engineer Associate Questions]

A Generative AI Engineer is building an LLM to generate article summaries in the form of a type of poem, such as a haiku, given the article content. However, the initial output from the LLM does not match the desired tone or style.

Which approach will NOT improve the LLM's response to achieve the desired response?

AProvide the LLM with a prompt that explicitly instructs it to generate text in the desired tone and style

BUse a neutralizer to normalize the tone and style of the underlying documents

CInclude few-shot examples in the prompt to the LLM

DFine-tune the LLM on a dataset of desired tone and style

Show Suggested Answer

Suggested Answer: B

The task at hand is to improve the LLM's ability to generate poem-like article summaries with the desired tone and style. Using a neutralizer to normalize the tone and style of the underlying documents (option B) will not help improve the LLM's ability to generate the desired poetic style. Here's why:

Neutralizing Underlying Documents: A neutralizer aims to reduce or standardize the tone of input data. However, this contradicts the goal, which is to generate text with a specific tone and style (like haikus). Neutralizing the source documents will strip away the richness of the content, making it harder for the LLM to generate creative, stylistic outputs like poems.

Why Other Options Improve Results:

A (Explicit Instructions in the Prompt): Directly instructing the LLM to generate text in a specific tone and style helps align the output with the desired format (e.g., haikus). This is a common and effective technique in prompt engineering.

C (Few-shot Examples): Providing examples of the desired output format helps the LLM understand the expected tone and structure, making it easier to generate similar outputs.

D (Fine-tuning the LLM): Fine-tuning the model on a dataset that contains examples of the desired tone and style is a powerful way to improve the model's ability to generate outputs that match the target format.

Therefore, using a neutralizer (option B) is not an effective method for achieving the goal of generating stylized poetic summaries.

by Tom at Oct 20, 2024, 04:38 AM

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Elli

3 months ago

Neutralizing the tone? That seems counterproductive!

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Hershel

3 months ago

C sounds like a solid choice for better examples.

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Alpha

3 months ago

Really? I doubt fine-tuning will fix the tone issue.

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Yong

4 months ago

I think A is definitely the way to go!

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Phuong

4 months ago

Option B won't help at all.

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Nada

4 months ago

Fine-tuning sounds effective, but I wonder if it could also lead to overfitting. Still, I think option D is a solid approach for achieving the desired tone.

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Roselle

4 months ago

I practiced a similar question where including examples helped clarify the task for the LLM, so I think option C would definitely improve the response.

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Lynna

4 months ago

I'm a bit unsure about the neutralizer in option B. I feel like normalizing tone might not be the best way to achieve a specific style.

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Son

5 months ago

I remember discussing how providing explicit prompts can really help guide the LLM, so I think option A would actually improve the output.

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Zack

5 months ago

Fine-tuning the LLM on a dataset of the desired tone and style seems like the most logical approach to me. That should help the model learn the specific characteristics we're looking for.

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Kristeen

5 months ago

Okay, I'm feeling pretty confident about this one. Including few-shot examples in the prompt seems like a great way to guide the LLM towards the desired output. That's my pick.

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Pearly

5 months ago

Hmm, this is a tricky one. I'm not sure if using a neutralizer would be the right approach here. Wouldn't that just remove any distinctive tone or style altogether?

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Markus

5 months ago

I think the key here is to figure out which approach won't help achieve the desired tone and style. Providing a prompt that explicitly instructs the LLM seems like the way to go, so I'm going to rule that one out.

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