Website-Building AI Assistants With Content-Grounded Response Evaluation
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Solution Overview
Problem
Existing AI-powered website interaction systems struggle to maintain coherent dialogues, provide timely and consistently accurate information, adapt their communication style to match brand voice and goals, and lack robust mechanisms for validating AI-generated responses against website content, often leading to inaccurate or irrelevant information.
Innovation Solution
A system that integrates a language learning model (LLM) with a website building system (WBS) using an input processor, content engine, prompt generator, and chat triad to evaluate response relevance and accuracy, employing hybrid search methodologies and vector embeddings to ensure responses are contextually appropriate and aligned with website content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a language learning model (LLM) is integrated with a website building system to provide AI-powered interactions, then user engagement and personalized experiences are enhanced, but response accuracy and relevance to website content deteriorate
Solution Approach 1:
The patent introduces an intermediary evaluation system consisting of a relevance evaluator and an answer quality evaluator that mediates between the LLM and the user. These evaluators act as intermediaries to assess whether the LLM's responses are relevant to the website content and of sufficient quality before presenting them to users, thereby resolving the contradiction between enhancing user experiences and maintaining response accuracy.
Solution Approach 2:
The system implements a feedback mechanism where the evaluators provide continuous assessment of LLM responses based on website content relevance and answer quality metrics. This feedback loop allows the system to monitor and control response accuracy while maintaining the adaptability and personalization benefits of the LLM, preventing degradation of response quality.
2Productivity
If AI assistants are integrated into website functionality, then user engagement is enhanced, but control over answer engines deteriorates
Solution Approach 1:
The evaluation system provides feedback on LLM response relevance and quality, enabling website administrators to monitor and control the AI assistant's performance. This feedback mechanism maintains control over the answer engine while still enhancing user engagement through AI-powered interactions.
3Speed
If LLM is used to generate responses, then response speed is improved, but response relevance to website content deteriorates
Solution Approach 1:
The relevance evaluator serves as an intermediary that quickly assesses whether LLM-generated responses are relevant to the website content. This intermediary layer maintains the speed advantage of LLM-generated responses while preventing irrelevant information from being presented to users.
Solution Approach 2:
The system applies partial evaluation - not every response requires full detailed assessment, but rather a quick relevance check followed by more thorough evaluation only when needed. This approach maintains response speed while ensuring relevance quality through selective application of evaluation criteria.
Data Source
AI summary
A system for evaluating responses generated by a language learning model (LLM) for a query from an end-user of a website of a website building system (WBS) includes an input processor, a content engine, a prompt generator, and a chat triad. The input processor receives the query. The content engine retrieves relevant content from a content management system (CMS) of the WBS. The prompt generator prompts the LLM to generate an answer according to the query and content. The chat triad evaluates relevance and accuracy of the generated answer by assigning relevance values to relationships between the query, content, and answer; determining a combined score; evaluating the score against criteria; and determining whether to present the answer to the end-user.