AIF-C01 · Question #155
A company is using a large language model (LLM) on Amazon Bedrock to build a chatbot. The chatbot processes customer support requests. To resolve a request, the customer and the chatbot must…
The correct answer is B. Add messages to the model prompt. The company is building a chatbot using an LLM on Amazon Bedrock, and the chatbot needs to use content from previous customer messages to resolve requests. Adding previous messages to the model prompt (also known as providing conversation history) enables the LLM to maintain…
Question
A company is using a large language model (LLM) on Amazon Bedrock to build a chatbot. The chatbot processes customer support requests. To resolve a request, the customer and the chatbot must interact a few times. Which solution gives the LLM the ability to use content from previous customer messages?
Options
- ATurn on model invocation logging to collect messages.
- BAdd messages to the model prompt.
- CUse Amazon Personalize to save conversation history.
- DUse Provisioned Throughput for the LLM.
How the community answered
(33 responses)- A12% (4)
- B82% (27)
- C3% (1)
- D3% (1)
Explanation
The company is building a chatbot using an LLM on Amazon Bedrock, and the chatbot needs to use content from previous customer messages to resolve requests. Adding previous messages to the model prompt (also known as providing conversation history) enables the LLM to maintain context across interactions, allowing it to respond coherently based on the ongoing conversation. To enable a large language model (LLM) to maintain context in a conversation, you can include previous messages in the model prompt. This approach, often referred to as providing conversation history, allows the LLM to generate responses that are contextually relevant toprior
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