The quality of responses in artificial intelligence applications has become an essential aspect to ensure user satisfaction. This is especially critical in chat assistants, such as those used in human resources, where it is vital that responses align with company policies and maintain a consistent tone. In this context, Amazon Bedrock has introduced a new solution that combines user feedback with few-shot techniques, promising to significantly improve the quality of responses.
The standout model is the Amazon Titan Text Embeddings v2, which facilitates the generation of semantic representations of queries. This tool is essential for optimizing responses, as it allows for the identification and use of similar examples to guide the generation of more personalized and accurate responses. Recent research suggests that user feedback can be applied iteratively to improve the alignment and robustness of AI-generated responses.
In implementing this proposal, a publicly available user feedback dataset was used to demonstrate the model’s effectiveness. Through sampling methods and semantic similarity, a statistically significant increase in user satisfaction scores was achieved, reaching an increase of 3.67%.
The development of this technology involved several steps, including collecting feedback data, creating embeddings for the queries, and using similar examples in a few-shot approach to generate optimized prompts. The results obtained were compared with responses generated by non-optimized language models, using statistical tests such as the paired t-test to validate the improvements.
Among the benefits offered by Amazon Bedrock are the absence of infrastructure management, a pay-as-you-go model, enterprise security, and ease of integration with existing applications. This approach promises not only improved performance in AI assistants but could also positively impact business operations by reducing risks of political misunderstandings and potentially decreasing the volume of escalated support tickets.
However, despite these advancements, limitations still exist, especially in closed-domain applications where user feedback may be limited. The lack of representative data could hinder the effectiveness of the optimizations. Looking ahead, the possibility of extending this system to different languages and improving context management through emerging techniques opens the door to even more advanced development in the interaction between artificial intelligence and users.
via: MiMub in Spanish
