Prompt Modality Selection Using Intent Matching to Cut GPT Latency
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Solution Overview
Problem
Contact centers face high labor costs and latency issues due to the use of generative pretrained transformer (GPT) engines for processing user prompts, leading to increased computing resources and slow response times.
Innovation Solution
Implementing an intent matching engine to filter prompts and reduce reliance on GPT engines by matching intents with a confidence score, coupled with a noise removal filter to enhance efficiency and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPT engines are used for processing user prompts, then natural language understanding and response generation are improved, but computing resource consumption and latency increase
Solution Approach 1:
The system segments prompt processing into two paths: a fast path using traditional NLP techniques for straightforward intents, and a slow path using GPT engines for complex or ambiguous intents. This segmentation allows the system to use computationally expensive GPT processing only when necessary, thereby reducing overall resource consumption while maintaining high-quality natural language understanding for complex cases.
Solution Approach 2:
An intent classification model serves as an intermediary between the user prompt and the GPT engine. This intermediary quickly assesses whether a prompt requires complex GPT processing or can be handled by traditional NLP methods, thereby filtering out unnecessary GPT engine invocations and reducing computing resource consumption while preserving accurate intent recognition.
2Reliability
If GPT engines are used for processing user prompts, then response quality is improved, but response time increases due to latency
Solution Approach 1:
The system segments prompt processing into two parallel paths: a fast path using traditional NLP techniques that provides quick responses for straightforward intents, and a slow path using GPT engines that provides high-quality responses for complex intents. This segmentation ensures that most prompts receive rapid responses while complex prompts still benefit from superior GPT-generated answers.
Solution Approach 2:
The intent classification model performs preliminary action by quickly assessing whether a prompt requires GPT engine processing before the actual response generation occurs. This preliminary classification filters out prompts that can be handled by faster traditional NLP methods, thereby reducing overall response time while maintaining high response quality for prompts that truly need GPT processing.
3Speed
If traditional NLP techniques are used for intent matching, then processing speed is improved, but accuracy for complex prompts deteriorates
Solution Approach 1:
The intent classification model acts as an intermediary that directs prompts to the appropriate processing path. For simple, clear intents, traditional NLP techniques provide fast and accurate processing. For complex or ambiguous intents, the intermediary routes the prompt to the GPT engine, which provides superior intent matching accuracy. This intermediary approach allows the system to leverage the speed of traditional NLP while compensating for its accuracy limitations on complex prompts.
Solution Approach 2:
The system changes the processing parameters dynamically based on prompt characteristics. For straightforward prompts with clear intent, the system uses traditional NLP parameters and methods that provide fast processing. For complex prompts with ambiguous intent, the system switches to GPT engine parameters and methods that provide higher accuracy. This parameter changing approach allows the system to optimize for speed when appropriate and for accuracy when necessary.
Data Source
AI summary
A server obtains a prompt from a user device. The server determines a score representing a likelihood that the prompt matches an intent. The server selects, based on the score, a modality to generate a response to the prompt. The modality is selected from a group comprising a workflow for the intent and a generative pretrained transformer engine. The server generates the response using the modality. The server transmits the response to the user device.