Multimodal Context Selection for Faster LLM Issue Resolution
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Solution Overview
Problem
In fast-paced cloud or multi-cloud computing environments, human-based technical issue resolution varies in quality and consistency, leading to potential service downtime due to wait times and the inability to leverage valuable insights from non-text data modalities.
Innovation Solution
A system utilizing a large language model (LLM) processes text and image-based multimodal queries through a multimodal context selector, translating technical documentation into relevant metadata for rapid, accurate issue resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human-based support is used to address technical issues, then responses can be provided with human judgment and adaptability, but response time increases and consistency varies
Solution Approach 1:
The system enables self-service technical issue resolution by automatically processing user queries through an LLM that generates responses without human intervention. The multimodal context selector automatically retrieves relevant documentation and code snippets, allowing the system to serve itself and users without requiring human support staff for routine issues.
Solution Approach 2:
Technical documentation, code snippets, and contextual information are pre-processed and stored in structured formats before queries arrive. The multimodal context selector maintains pre-indexed repositories of documentation and code, enabling rapid retrieval and response generation without requiring real-time human analysis or preparation.
2Loss of information
If traditional text-only processing is used, then system complexity remains low, but valuable insights from non-text data modalities are not leveraged
Solution Approach 1:
The LLM serves multiple functions: it processes text queries, generates text responses, and interprets multimodal context including code snippets and technical documentation. The system handles diverse data types (text, code, documentation) through a single unified model, enabling comprehensive information processing without requiring separate specialized systems for each modality.
Solution Approach 2:
The multimodal context selector acts as an intermediary between the user query and the LLM, retrieving and preprocessing relevant context from multiple sources including documentation repositories and code repositories. This intermediary layer organizes and prepares multimodal information before presenting it to the LLM, making complex information accessible without increasing LLM complexity.
3Measurement precision
If comprehensive context is provided to the LLM, then response accuracy improves, but processing time increases
Solution Approach 1:
The multimodal context selector extracts only the most relevant context snippets needed to answer the user query, rather than providing all available documentation and code. It selectively retrieves specific documentation sections, relevant code snippets, and contextual information based on the query semantics, providing sufficient accuracy with minimal processing overhead.
Solution Approach 2:
The system provides slightly more context than strictly necessary by including multiple relevant code snippets and documentation sections, ensuring that the LLM has sufficient information for accurate responses. This partial excess of context ensures completeness without significantly impacting processing time, as the LLM can efficiently process the curated context set.
Data Source
AI summary
A method for technical issue resolution. The method includes: receiving, from a user, a text query concerning a technical issue; obtaining query-related context relevant to the text query; and processing, through a large language model (LLM), the text query and the query-related context to produce a multimodal query response used by the user to address the technical issue. More specifically, embodiments described herein utilize text topic and zero shot classification models to translate multimodal technical documentation (e.g., including text and images) into topic relevant metadata; and process queries, pertaining to technical issues, using a multimodal LLM provided with query-related text and image context derived from said topic relevant metadata.