Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A large e-commerce company is experiencing high costs for their generative AI product recommendation system. The system generates personalized product descriptions for millions of users daily. The team wants to reduce cost while maintaining quality. They are using a fine-tuned version of a large foundation model hosted on Vertex AI. The current cost is driven by the number of tokens processed. Which approach should they take?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Optimize prompts to generate shorter, more concise descriptions
Prompt engineering to reduce output length decreases token usage per request, directly lowering cost without model changes. Option B (switching to a larger model) increases cost. Option C (increasing batch size) may not reduce per-request cost. Option D (retraining with more data) does not affect inference cost.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Optimize prompts to generate shorter, more concise descriptions
Why this is correct
Shorter outputs use fewer tokens, reducing cost.
- ✗
Switch to a larger, more capable foundation model
Why it's wrong here
Larger models generate more tokens and increase cost.
- ✗
Retrain the model with more product data to improve efficiency
Why it's wrong here
Retraining does not reduce inference token count.
- ✗
Increase the batch size of inference requests
Why it's wrong here
Batching does not reduce per-request token usage.
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