Retrieval Augmented Generation (RAG) is a Machine Learning (ML) framework which combines information retrieval with text generation to enhance the quality of generated responses by fetching relevant external knowledge which it then uses to inform the output. One piece to the RAG (and larger Generative AI) puzzle is curated, prebuilt taxonomies. With these taxonomies, RAG models have been known to have some key benefits:
Improved Retrieval Accuracy
The generated responses will only be as accurate as the retrieval content that is added. By including a prebuilt, curated taxonomy, the information in the retrieval database is categorized based on predefined classes and relationships, which allows the RAG model to more effectively narrow down the search space and select content more closely aligned with the original query.
Enhanced Understanding of Context
The hierarchical structure of taxonomies aids the RAG model in understanding the context of a query. Contextual awareness is crucial, especially when dealing with complex questions and subtle nuances in topics or categories.
Increased Consistency in Responses
Using a taxonomy ensures that there is a consistency in the categorization and terminology used by the RAG model. This consistency travels across queries and domains which leads to more reliable and uniform responses, aiding in an improved UX.
Scalability and Efficiency
Prebuilt taxonomies allow RAG models to efficiently scale efforts across diverse domains without needing to retrain from scratch every time. This ready-made framework of knowledge can be quickly integrated and adapted, saving time and resources in model training.
Knowledge Enhancement and Update
WAND's curated, prebuilt taxonomies are constantly updated to add new information and track current trends. These updates can be seamlessly integrated into the model. Keeping the model's knowledge base current improves accuracy over time.
Domain-Specific Optimization
Narrowing in on domain-specific RAG models, taxonomies can be tailored to those specific areas and ensure that retrieved content is both relevant and compliant with specific standards and terminologies.
Simply put, choosing to integrate taxonomies into RAG models leverages structured, relevant, organized data effectively, which improves efficiency and quality in generated responses. This has been found to be particularly valuable in applications requiring a high level of accuracy and reliability in information retrieval and generation.