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Optimize query responses with user feedback using Amazon Bedrock embedding and few-shot prompting
- Improving response quality for user queries is crucial for AI-driven applications and can be refined using Amazon Bedrock, user feedback dataset, and few-shot prompting.
- By leveraging Amazon Titan Text Embeddings v2, a significant enhancement in response quality and user satisfaction can be achieved.
- The approach involves utilizing user feedback and few-shot prompting to optimize responses effectively, showcasing a 3.67% increase in user satisfaction scores.
- Amazon Bedrock offers automatic prompt optimization, but custom optimization based on OSS libraries and user feedback can provide tailored enhancements.
- Key steps include data collection, data sampling, embedding generation, and few-shot prompting based on similarity search for response optimization.
- The system uses an LLM to judge and evaluate responses for alignment with the user query, providing statistically significant improvements.
- Results indicated a 3.67% increase in satisfaction scores using the optimized prompt, statistically validated via a paired sample t-test.
- Key takeaways include the effectiveness of few-shot prompting, contextual similarity through Amazon Titan Text Embeddings, and the business impact of improved response quality.
- Limitations include reliance on user feedback availability and volume, while future work involves multilingual support and addressing low-feedback scenarios.
- The system's flexibility, practicality, and potential for real-world applications make it suitable for diverse domains needing user-aligned responses.
- The approach offers a self-improving system without complex ML expertise, capable of continuous learning and enhanced ROI over time.
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