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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?
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RRitesh
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.
Explanation:
Explanation
Correct Answer: D - Upload PDF documents to an Amazon Bedrock knowledge base. Use the knowledge base to provide context when users submit prompts to Amazon Bedrock.
Why Option D is Correct:
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Cost-Effectiveness: Amazon Bedrock knowledge bases are designed specifically for Retrieval-Augmented Generation (RAG) applications. They provide an efficient way to retrieve relevant information from documents without needing to include entire documents in every prompt.
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Efficient Context Management: Knowledge bases automatically:
- Convert PDF files into embeddings
- Store them in a vector database
- Retrieve only the most relevant sections when processing user queries
- This avoids the high token costs of including entire PDFs in prompts
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Scalability: As the number of PDF files grows, knowledge bases handle the scaling efficiently without increasing costs linearly.
Why Other Options Are Not Most Cost-Effective:
Option A & B (Prompt Engineering with PDF context):
- Token Cost: Including PDF content directly in prompts consumes a large number of tokens
- Context Window Limits: LLMs have token limits (typically 4K-128K tokens), and entire PDFs may exceed these limits
- Inefficient: Every query would need to include the PDF content, leading to redundant processing
Option C (Fine-tuning a model):
- High Initial Cost: Fine-tuning requires significant computational resources and is expensive
- Maintenance Cost: Each time manuals are updated, the model would need to be re-fine-tuned
- Overkill for This Use Case: Fine-tuning is better for changing model behavior or style, not for adding factual knowledge
Key Benefits of Amazon Bedrock Knowledge Base:
- Automatic Document Processing: Handles PDF parsing, chunking, and embedding generation
- Smart Retrieval: Uses semantic search to find the most relevant information
- Cost Optimization: Only retrieves and processes relevant document sections
- Real-time Updates: Easy to add or update documents without retraining models
This approach provides the best balance of accuracy, performance, and cost for a chat interface with product manuals.
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