A company wants to use large language models (LLMs) with Amazon Bedrock to develop a chat interface for the company's product manuals. The manuals are stored as PDF files. Which solution meets these requirements MOST cost-effectively?
A.
Use prompt engineering to add one PDF file as context to the user prompt when the prompt is submitted to Amazon Bedrock.
B.
Use prompt engineering to add all the PDF files as context to the user prompt when the prompt is submitted to Amazon Bedrock.
C.
Use all the PDF documents to fine-tune a model with Amazon Bedrock. Use the fine-tuned model to process user prompts.
D.
Upload PDF documents to an Amazon Bedrock knowledge base. Use the knowledge base to provide context when users submit prompts to Amazon Bedrock.
A opção mais econômica seria D. Faça upload de documentos PDF para uma base de conhecimento do Amazon Bedrock. Use a base de conhecimento para fornecer contexto quando os usuários enviarem solicitações para o Amazon Bedrock.
Essa abordagem permite que o modelo acesse os documentos de forma eficiente sem a necessidade de incorporar todo o conteúdo nos prompts, o que pode ser caro. A base de conhecimento melhora a recuperação de informações sem exigir o ajuste completo do modelo, tornando a solução mais escalável e econômica.
Upload PDF documents to an Amazon Bedrock knowledge base. Use the knowledge base to provide context when users submit prompts to Amazon Bedrock: While this could be an efficient solution, it may not be as cost-effective as using prompt engineering with just the necessary context per query. Building and maintaining a knowledge base could incur additional costs, especially if the company only needs a temporary context for each query.
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