A company is using few-shot prompting on a base model that is hosted on Amazon Bedrock. The model currently uses 10 examples in the prompt. The model is invoked once daily and is performing well. The company wants to lower the monthly cost.Which solution will meet these requirements?
Answer(s): B
Decreasing the number of tokens in the prompt reduces the amount of data being processed, thereby lowering the cost of using the model. Since the model is performing well, reducing the prompt size is a cost-effective way to maintain performance while lowering expenses. The other options either increase costs or are unrelated to prompt size.
An AI practitioner has an application that uses an LLM. The AI practitioner observes inconsistent responses from the application when asking the application the same question repeatedly. The application responses differ in length, tone, and content.Which solution will improve application responses with the LEAST operational overhead?
Answer(s): A
Lowering the temperature reduces randomness in the LLM’s token selection, making responses more deterministic and consistent when the same prompt is submitted repeatedly. This requires only a parameter adjustment in the API request and therefore adds minimal operational overhead.
A company must comply with regulatory standards to develop and use trustworthy AI management solutions.Which approach will meet this requirement?
Answer(s): D
Complying with trustworthy AI regulations requires embedding fairness, transparency, accountability, and security across the entire AI lifecycle, from design and development through deployment and ongoing operation.
A company needs a generative AI (GenAI) application to explain its reasoning steps before giving final answers.Which prompt engineering technique will meet this requirement?
Chain-of-thought prompting encourages a generative AI model to work through intermediate reasoning steps before producing the final answer, which can improve transparency and performance on complex tasks.
A company wants to use its documents as a knowledge base for a large language model (LLM) in a RetrievalAugmented Generation (RAG) solution.Which solution will meet these requirements?
In a RAG solution, documents are split into chunks and converted into embeddings so they can be indexed and retrieved based on semantic similarity to user queries.
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