Recent advancements in retrieval-augmented generation have demonstrated impressive performance on the question-answering task. However, most previous work predominantly focuses on text-based answers. Although some studies have explored multimodal data, they still fall short in generating comprehensive multimodal answers, especially step-by-step tutorials for accomplishing specific goals. This capability is especially valuable in application scenarios such as enterprise chatbots, customer service systems, and educational platforms. In this paper, we propose a simple and effective framework, MuRAR (Multimodal Retrieval and Answer Refinement). MuRAR starts by generating an initial text answer based on the user’s question. It then retrieves multimodal data relevant to the snippets of the initial text answer. By leveraging the retrieved multimodal data and contextual features, MuRAR refines the initial text answer to create a more comprehensive and informative response. This highly adaptable framework can be easily integrated into an enterprise chatbot to produce multimodal answers with minimal modifications. Human evaluations demonstrate that the multimodal answers generated by MuRAR are significantly more useful and readable than plain text responses. A video demo of MuRAR is available at https://youtu.be/ykGRtyVVQpU.

Recent advancements in Large Language Models and Retrieval-Augmented Generation have boosted interest in domain-specific question-answering for enterprise products. However, AI Assistants often face challenges in multi-product QA settings, requiring accurate responses across diverse domains. Existing multi-domain RAG-QA approaches either query all domains indiscriminately, increasing computational costs and LLM hallucinations, or rely on rigid resource selection, which can limit search results. We introduce MKP-QA, a novel multi-product knowledge-augmented QA framework with probabilistic federated search across domains and relevant knowledge. This method enhances multi-domain search quality by aggregating query-domain and query-passage probabilistic relevance. To address the lack of suitable benchmarks for multi-product QAs, we also present new datasets focused on three Adobe products: Adobe Experience Platform, Target, and Customer Journey Analytics. Our experiments show that MKP-QA significantly boosts multi-product RAG-QA performance in terms of both retrieval accuracy and response quality.

The development of conversational AI assistants is an iterative process with many components involved. As such, the evaluation and continual improvement of these assistants is a complex and multifaceted problem. This paper introduces the challenges in evaluating and improving a generative AI assistant for enterprise that is under active development and how we address these challenges. We also share preliminary results and discuss lessons learned.