Google Professional Machine Learning Engineer
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You have deployed a conversational application using a large language model (LLM) for 1,000 users. User feedback indicates that while the responses are factually correct, users desire different levels of verbosity depending on the question type. Your goal is to make the model's responses more consistent with user expectations using a scalable solution. What should you do?
A
Implement a keyword-based routing layer. If the user's input contains the words "detailed" or "description," return a verbose response. If the user's input contains the word "fact." re-prompt the language model to summarize the response and return a concise response.
B
Ask users to provide examples of responses with the appropriate verbosity as a list of question and answer pairs. Use this dataset to perform supervised fine tuning of the foundational model. Re-evaluate the verbosity of responses with the tuned model.
C
Ask users to indicate all scenarios where they expect concise responses versus verbose responses. Modify the application 's prompt to include these scenarios and their respective verbosity levels. Re-evaluate the verbosity of responses with updated prompts.
D
Experiment with other proprietary and open-source LLMs. Perform A/B testing by setting each model as your application's default model. Choose a model based on the results.