Pre-Generative AI Prompt Comparison for Consistent Responses
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Solution Overview
Problem
Large language models (LLMs) used in natural language processing often generate inconsistent responses due to their vast training datasets, which is problematic in fields requiring precise language, such as finance and wealth management, where consistency and tailored responses are necessary.
Innovation Solution
A prompt engine that utilizes an LLM to generate responses, filters them based on predefined rules, and stores similar prompts for consistent responses, leveraging vector similarity algorithms to match received prompts with stored ones, and applies service-specific rules to ensure accuracy and consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If LLMs are trained on vast datasets to improve NLP task performance, then text generation capability is improved, but response consistency deteriorates
Solution Approach 1:
The system performs preliminary actions by storing previously generated prompts and responses in a database before new requests arrive. When a new prompt is received, the system first checks the database for similar historical prompts using similarity algorithms, and reuses existing responses when matches are found, thereby ensuring consistency while maintaining generation capability.
Solution Approach 2:
The system creates and stores copies of previously generated prompt-response pairs in a database. Instead of generating new responses every time, the system copies and reuses responses from the database when similar prompts are detected, ensuring consistent outputs across identical or similar inquiries while preserving the LLM's generation capabilities for novel questions.
2Adaptability or versatility
If LLMs generate responses dynamically to ensure tailored content, then adaptability is improved, but computing resource consumption increases
Solution Approach 1:
The system performs preliminary actions by pre-storing prompt-response pairs in a database before new requests arrive. When a new prompt is received, the system first checks the database for similar historical prompts using similarity algorithms, and reuses existing responses when matches are found, thereby ensuring consistency while maintaining generation capability.
Solution Approach 2:
The system implements self-service by automatically checking its own historical database for similar prompts and reusing existing responses without requiring new generation. This self-checking and self-reusing mechanism reduces computing resource consumption by avoiding redundant generation work for common or repeated queries.
3Manufacturing precision
If response filtering and rule application are applied to ensure precision, then language accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-storing prompt-response pairs in a database before new requests arrive. When a new prompt is received, the system first checks the database for similar historical prompts using similarity algorithms, and reuses existing responses when matches are found, thereby ensuring consistency while maintaining generation capability.
Solution Approach 2:
The system applies feedback mechanisms by checking generated or retrieved responses against predefined rules and service-specific requirements. The filtering process provides feedback on whether responses meet precision standards, and adjustments are made accordingly, ensuring language accuracy while managing processing time through efficient rule application.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for using generative AI for prompt comparison. The system may receive a prompt. The prompt may include a request for information. The system may identify a stored prompt based on a similarity value between a received prompt for a large language model (LLM) and the stored prompt, the stored prompt including a response generated by the large language model (LLM). The system may generate a response using the LLM if the similarity value between the received and stored prompts is below a predefined threshold. The system may then modify the response by applying a first rule associated with a first designated phrase, and a second rule associated with a second designated phrase, where the first designated phrase comprises a banned phrase and where the second designated phrase comprises a selected phrase.