Enhancing LLM quality and interpretability with the Vertex Gen AI Evaluation Service

Developers leveraging large language models (LLMs) face two main challenges: managing the randomness of LLM outputs and mitigating their tendency to produce incorrect information. The unpredictability of LLMs can be both a creative asset and a hindrance, particularly when consistency and factual accuracy are essential. This randomness, while useful for generating creative content, can lead to "hallucinations" where the model confidently outputs misinformation, thus reducing trust in its reliability. Many tasks, like summarizing information or creating marketing content, don't have a single correct answer, making the variability of LLMs both a challenge and an opportunity. A financial institution, for instance, needed to ensure summaries of customer conversations were accurate, concise, and well-written. They addressed this by generating multiple LLM responses and using the Vertex Gen AI Evaluation Service to select the best one. By generating several versions of a summary with controlled randomness, they increased the likelihood of finding an optimal response. These responses were then compared using pairwise evaluation to identify the most accurate and relevant one. Finally, the top response was assessed using pointwise evaluation to ensure it met quality standards, with scores and explanations provided for transparency. This workflow, which can be adapted for different use cases and modalities, transforms LLM variability into a strength by systematically evaluating and selecting the best output, thus enhancing the quality, reliability, and trustworthiness of LLM-generated content.