Introduction:
In the intricate world of artificial intelligence (AI), achieving precision in responses is paramount. Enter RAG triads—Answer Relevance, Context Relevance, and Groundedness— the quintessential metrics for evaluating and enhancing AI performance. These triads, coupled with sophisticated feedback functions, revolutionize how we assess the effectiveness of AI systems. Let's embark on a journey to unravel the secrets of RAG triads and discover how they shape the landscape of AI precision.
Feedback function:
It is function that provides a score after reviewing an apps input, intermediate results and output.
Answer Relevance:
At the heart of RAG triads lies Answer Relevance, the beacon that guides us in determining the alignment between user queries and system responses.
Crafting a feedback function for Answer Relevance involves leveraging large models to implement structured assessments. By scrutinizing user prompts and app responses, we ensure that the final output resonates with the user's intent, enriching the overall user experience.
Context Relevance:
Context Relevance serves as the compass in navigating the sea of information retrieval, determining the quality of retrieved data.
Constructing a feedback function for Context Relevance mirrors the process for Answer Relevance. By harnessing the power of large models, we evaluate user queries alongside retrieved context, amalgamating scores to gauge the relevance of intermediate results. This ensures that AI systems deliver responses that are not only accurate but also contextually appropriate.
Groundedness:
Groundedness, the cornerstone of AI fidelity, assesses the degree to which responses are supported by retrieved documents. It acts as a safeguard against hallucination, ensuring that AI-generated answers remain rooted in factual evidence.
Constructing a feedback function for Groundedness involves comprehensive scrutiny of context selections and system output. By aggregating the groundedness of individual sentences, we guarantee that AI systems deliver responses that are not only accurate but also trustworthy.
Summary:
RAG triads—Answer Relevance, Context Relevance, and Groundedness—usher in a new era of precision in artificial intelligence. Through structured feedback functions, these metrics enable us to evaluate and enhance the performance of AI systems, ensuring that responses are not only relevant and contextually appropriate but also grounded in factual evidence. As we continue to navigate the ever-evolving landscape of AI, mastering the intricacies of RAG triads will be instrumental in unlocking the full potential of intelligent systems. Join us on this journey to elevate AI precision and reshape the future of technology.