LLM Input Buffering for Incomplete Messaging Prompts
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Solution Overview
Problem
Existing LLMs struggle with incomplete or partial inputs in electronic messaging conversations, leading to inefficient use and incorrect responses due to the lack of mechanisms to buffer or indicate waiting for additional information.
Innovation Solution
A prompt evaluation and buffering system that coordinates between the front end and back end of the messaging system to detect incomplete inputs, buffer them temporarily, and update the user interface to indicate waiting, using lighter weight LLMs, NLP tools, and look-up-tables to ensure complete inputs are processed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the LLM processes every input immediately, then the response speed is fast, but the correctness deteriorates due to incomplete inputs
Solution Approach 1:
The system performs preliminary evaluation of input completeness before submitting to the LLM. A buffer mechanism evaluates incoming inputs against completion criteria (such as punctuation, question marks, or configured patterns) and only submits complete inputs to the LLM for processing, preventing wasted processing on incomplete data
Solution Approach 2:
The system dynamically adjusts its processing behavior based on input completeness status. Complete inputs are processed immediately through the LLM, while incomplete inputs are buffered and held for further input or timeout, creating a flexible response strategy that adapts to conversation flow
2Reliability
If the system buffers incomplete inputs, then the response correctness improves, but the device complexity increases due to buffering mechanisms
Solution Approach 1:
The system segments the input processing pipeline into distinct stages: an input buffering stage that collects and evaluates incoming messages, and an LLM processing stage that handles complete inputs. This segmentation allows independent optimization of each stage and simplifies the overall architecture by separating concerns
Solution Approach 2:
The buffer acts as an intermediary component between the user interface and the LLM processing engine. It mediates the flow of inputs, evaluating completeness and controlling when inputs are forwarded to the LLM, thereby protecting the complex LLM processing from receiving incomplete data while maintaining a simple interface
3Loss of energy
If the system waits for complete inputs, then the resource efficiency improves, but the productivity decreases due to waiting time
Solution Approach 1:
The system implements feedback mechanisms that monitor input completeness in real-time. When an incomplete input is detected, the buffer provides feedback to the user interface to indicate waiting state, and when completeness is achieved or timeout occurs, feedback triggers appropriate action (processing or moving to next input), creating an efficient control loop
Solution Approach 2:
The system implements timeout mechanisms that allow it to skip indefinitely waiting for complete inputs. If no completing input is received within the timeout period, the buffer moves forward by processing what is available or discarding the incomplete input, preventing excessive waiting and maintaining conversation flow
Data Source
AI summary
A system and method are provided for handling incomplete inputs to large language models (LLMs). The method includes, responsive to detecting an incomplete input in a messaging conversation, buffering the incomplete input prior to having an LLM respond to a prompt associated with the incomplete input.