Conversational Assistant Architecture for Hallucination Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current LLM-based conversational systems in highly regulated industries like healthcare and clinical trials face challenges with hallucinations, context window overload, scalability, and lack of effective mechanisms for subject matter expert validation, leading to inefficiencies and errors in complex domain-specific queries.
Innovation Solution
A conversational AI system with an outer and inner loop architecture, incorporating an authoring workbench, validation components, and persona-based access controls, to ensure accurate and secure responses by using agentic RAG techniques and deterministic scripts, while maintaining data privacy and integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If LLM-based conversational systems are used in clinical trials, then natural language processing capabilities are improved, but hallucination and reliability issues worsen
Solution Approach 1:
The patent introduces an intermediary validation layer between the LLM and the user. A separate validation component analyzes the LLM's generated responses against the clinical trial protocol and data, acting as a mediator to filter out hallucinations before presenting information to users. This resolves the contradiction by maintaining natural language processing capabilities while adding a reliability checkpoint.
Solution Approach 2:
The system implements feedback mechanisms where validation results are fed back into the response generation process. When the validation component detects potential hallucinations or inaccuracies, it provides feedback to adjust or reject the LLM's output, thereby improving response accuracy while preserving the natural language interface.
2Loss of information
If context window size is increased to handle complex queries, then information processing depth is improved, but system resource consumption worsens
Solution Approach 1:
The patent segments the context processing into multiple smaller context windows that are managed separately. Instead of loading all clinical trial data into a single large context window, the system divides the context into manageable chunks and processes them in stages, reducing peak memory usage while maintaining the ability to handle complex queries through sequential processing.
Solution Approach 2:
The system performs preliminary actions by pre-processing and indexing clinical trial data before it needs to be queried. Relevant information is pre-identified and organized into structured formats that can be efficiently retrieved and fed into the LLM's context window only when needed, reducing the need for large persistent context windows and lowering overall resource consumption.
3Measurement precision
If manual debugging by domain experts is implemented, then issue resolution accuracy is improved, but operational efficiency worsens
Solution Approach 1:
The patent implements self-service capabilities where the system automatically detects and resolves common issues without requiring manual intervention from domain experts. Automated monitoring and validation components identify problems in clinical trial operations and attempt corrective actions autonomously, reserving expert involvement for only the most complex cases, thereby maintaining high accuracy while improving operational efficiency.
Solution Approach 2:
The system replaces manual mechanical debugging processes with automated computational mechanisms. AI-based validation and error detection systems substitute for human experts in routine debugging tasks, maintaining the precision of expert review while eliminating the time-consuming manual process, thus resolving the contradiction between accuracy and efficiency.
4Ease of operation
If LLMs are integrated into existing enterprise systems, then conversational capability is improved, but system complexity worsens
Solution Approach 1:
The patent introduces an intermediary integration layer that sits between the LLM and existing enterprise systems. This mediator handles all communication and data exchange, translating between different system formats and protocols. It simplifies the integration process by providing a standardized interface, thereby improving conversational capability without proportionally increasing system complexity.
Solution Approach 2:
The system implements a universal integration framework that can interface with multiple different enterprise systems through a common architecture. This multi-functional approach allows the LLM to communicate with various clinical trial management systems, electronic health records, and other enterprise applications using a single standardized interface, reducing overall integration complexity while enhancing conversational capabilities across the entire system ecosystem.
Data Source
AI summary
Described herein are methods and systems for a conversational assistant that may be configured for use with one or more applications. The disclosed systems and methods may be configured as an outer loop/inner loop architecture incorporating one or more large language models (LLMs) and/or RAG functionality to support dialog-based interactions.