Understand harm mitigation strategies for generative AI models, including Retrieval Augmented Generation (RAG) techniques, to excel in the Microsoft Azure AI-900 certification.
Table of Contents
Question
Which of the following tasks is usually performed at the metaprompt and grounding layer of the harm mitigation methodology for generative AI models?
A. Implementing abuse detection algorithms and suppressing inappropriate prompts and responses
B. Utilizing a retrieval augmented generation (RAG) approach for getting data from trusted sources
C. Creating transparent documentation about the capabilities and limitations of the generative AI solution
D. Selecting a model based on complexity which is appropriate for its specific use case
Answer
B. Utilizing a retrieval augmented generation (RAG) approach for getting data from trusted sources
Explanation
A suitable task for the metaprompt and grounding layer is utilizing a retrieval augmented generation (RAG) approach for getting data from trusted sources. This technique incorporates information from reliable sources into prompts, enhancing context and potentially reducing the risk of inaccurate or harmful outputs. At the metaprompt and grounding layer of the harm mitigation methodology for generative AI models, the primary tasks focus on guiding the model towards safe and relevant outputs.
Creating transparent documentation about the capabilities and limitations of the generative AI solution is not performed at the metaprompt and grounding layer. This is primarily a user experience layer task, ensuring user awareness of the system’s functionalities and potential limitations.
Implementing abuse detection algorithms and suppressing inappropriate prompts and responses is not performed at the metaprompt and grounding layer. These tasks belong to the safety system layer, which focuses on identifying and addressing misuse or harmful outputs after they have been generated.
Selecting a model based on complexity which is appropriate for its specific use case is not performed at the metaprompt and grounding layer. This falls under the responsibility of the model layer, where choosing the right model size and complexity for simpler tasks helps mitigate potential harm from overly powerful models.
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