AI judging AI: Scaling unstructured text analysis with Amazon Nova

Picture this: Your team just received 10,000 customer feedback responses. The traditional approach? Weeks of manual analysis. But what if AI could not only analyze this feedback but also validate its own work? Welcome to the world of large language model (LLM) jury systems deployed using Amazon Bedrock.

As more organizations embrace generative AI, particularly LLMs for various applications, a new challenge has emerged: ensuring that the output from these AI models aligns with human perspectives and is accurate and relevant to the business context. Manual analysis of large datasets can be time consuming, resource intensive, and thus impractical. For example, manually reviewing 2,000 comments can take over 80 hours, depending on comment length, complexity, and researcher analyses. LLMs offer a scalable approach to serve as qualitative text annotators, summarizers, and even judges evaluating text outputs from other AI systems.

This prompts the question, “But how can we ...