Cascading AI Prompts for Hallucination-Resistant Document Analysis
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Solution Overview
Problem
Generative AI models often produce inaccurate results, known as 'hallucinations', due to issues in initial heuristic data collection, overfitting, and insufficient logical reasoning, which can lead to errors in generated content.
Innovation Solution
A system that utilizes a cascading series of prompts and a dynamic database of ground-truth facts to iteratively refine AI-generated outputs, correcting inaccuracies in real-time and improving long-term accuracy by leveraging a prompt tree and logical reasoning to guide the model towards accurate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generative AI models are trained to extrapolate and fill in gaps with generated content, then the model's ability to respond to novel prompts is improved, but hallucinations and inaccuracies increase
Solution Approach 1:
The system implements a feedback mechanism where the output of the generative AI model is fed back into the system through a second prompt that asks the model to evaluate the accuracy and truthfulness of its own previous output. This self-evaluation feedback loop allows the system to identify and correct hallucinations while maintaining the model's ability to generate novel content.
Solution Approach 2:
The system performs preliminary fact-checking by comparing the generated content against a knowledge base before finalizing the output. This preliminary action of verifying accuracy against stored information prevents hallucinations from reaching the final output while preserving the model's adaptability to novel prompts.
2Productivity
If the system uses a single prompt for data analysis, then the process is simple and fast, but the accuracy and depth of analysis are insufficient
Solution Approach 1:
The analysis process is segmented into multiple distinct prompts: a first prompt for initial content generation and a second prompt for accuracy evaluation. This segmentation allows the system to maintain speed by using efficient single-prompt generation while achieving high accuracy through the structured multi-stage analysis process.
Solution Approach 2:
The system applies partial action by selectively evaluating only the critical aspects of the generated content through the second prompt, rather than re-generating the entire output. This approach maintains productivity while achieving sufficient accuracy for the specific analytical task.
3Reliability
If the system iteratively refines AI-generated outputs through multiple prompts, then accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary preparation by organizing a knowledge base of factual information before the iterative refinement process begins. This preliminary action enables faster comparison and verification during the multi-prompt refinement process, reducing the time penalty associated with iterative accuracy improvement.
Solution Approach 2:
The system extracts only the essential evaluation criteria and key factual elements from the generated content for verification against the knowledge base. This extraction approach reduces the complexity and time required for each iterative refinement cycle while maintaining high accuracy standards.
Data Source
AI summary
A system may include a processor and a non-transitory computer readable medium having stored thereon instructions that are executable by the processor to cause the system to process a document to derive a plurality of document chunks; generate, for a generative machine learning (ML) model, a first prompt configured to cause the generative ML model to provide a first report based on a first of the plurality of document chunks; extract a feature from the first report and comparing the extracted feature to a table of known features; and in response to and based on the comparison, generate, for the generative ML model, a second prompt configured to cause the generative ML model to provide a second report based on a second of the plurality of document chunks.