AIF-C01 · Question #168
A company wants to improve the accuracy of the responses from a generative AI application. The application uses a foundation model (FM) on Amazon Bedrock. Which solution meets these requirements…
The correct answer is D. Use prompt engineering. The company wants to improve the accuracy of a generative AI application using a foundation model (FM) on Amazon Bedrock in the most cost-effective way. Prompt engineering involves optimizing the input prompts to guide the FM to produce more accurate responses without modifying…
Question
A company wants to improve the accuracy of the responses from a generative AI application. The application uses a foundation model (FM) on Amazon Bedrock. Which solution meets these requirements MOST cost-effectively?
Options
- AFine-tune the FM.
- BRetrain the FM.
- CTrain a new FM.
- DUse prompt engineering.
How the community answered
(49 responses)- A12% (6)
- B16% (8)
- C4% (2)
- D67% (33)
Explanation
The company wants to improve the accuracy of a generative AI application using a foundation model (FM) on Amazon Bedrock in the most cost-effective way. Prompt engineering involves optimizing the input prompts to guide the FM to produce more accurate responses without modifying the model itself. This approach is cost-effective because it does not require additional computational resources or training, unlike fine-tuning or retraining. Prompt engineering is a cost-effective technique to improve the performance of foundation models. By crafting precise and context-rich prompts, users can guide the model to generate more accurate and relevant responses without the need for fine-tuning or retraining.
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