LLM Funnel Chain for API Documentation Token Limits
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Solution Overview
Problem
Large language models (LLMs) face difficulties in providing accurate and complete responses when integrated into multi-service big platform systems due to their inability to handle lengthy API endpoint documentation, which exceeds the LLM's maximum token limit, leading to truncated context and inaccurate outputs.
Innovation Solution
The implementation of an LLM funnel chain in conjunction with hierarchically layered documentation, where prompts are iteratively generated and processed across multiple LLMs to stay within the token limit, allowing for accurate and coherent responses by breaking down documentation into manageable subsets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If complete API endpoint documentation is provided to the LLM, then the model can access comprehensive information, but the token count exceeds the maximum token limit causing truncated context
Solution Approach 1:
The patent segments the complete API endpoint documentation into multiple document groups, each containing a subset of the documentation. The system processes these segments iteratively through multiple LLM calls, accumulating relevant information across calls while staying within token limits. This segmentation allows the system to handle comprehensive documentation without exceeding the LLM's maximum token capacity.
2Measurement precision
If the LLM processes all API documentation at once, then complete information is available, but the response accuracy decreases due to token truncation
Solution Approach 1:
The system employs partial action by processing only the necessary subset of API documentation in each LLM call rather than attempting to process all documentation at once. The iterative approach allows the system to accumulate sufficient information across multiple calls, achieving accurate responses without being constrained by the token limit on any single call.
3Productivity
If the system uses a single LLM call with all documentation, then processing is faster, but the output becomes inaccurate due to context truncation
Solution Approach 1:
The system performs preliminary actions by first analyzing the user query to identify relevant API endpoints, then selectively retrieving and processing only the necessary documentation subsets. This preliminary filtering step reduces the volume of documentation that needs to be processed in each LLM call, maintaining processing efficiency while ensuring accurate responses through context completeness.
Data Source
AI summary
The present disclosure produces a first output in response to inputting a first prompt into a large language model (LLM). The first prompt comprises a first document group that corresponds to a second document group, and the LLM is limited by a maximum token limit that is less than a token count of the second document group. The present disclosure generates a second prompt that comprises a subset of the second document group corresponding to the first output. The present disclosure then produces a second output based on the subset of the second document group in response to inputting the second prompt into the LLM.