Quality Ticket Enrichment Using Vector Search and AI Deduplication
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing user feedback systems face challenges in efficiently matching and processing diverse descriptions of similar problems or issues, leading to duplicate reports and inefficient resource allocation for issue resolution.
Innovation Solution
Utilizing vector embeddings and language models to enrich quality tickets with data enrichment, including user intent identification, workaround suggestions, and resolution descriptions, thereby facilitating faster and more effective issue resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processing of quality tickets is used, then human judgment can be applied to each ticket, but the cycle time for resolving issues becomes hours long and engineering resources are consumed inefficiently
Solution Approach 1:
The system performs preliminary actions by automatically enriching incoming quality tickets with relevant information from the knowledge base before human engineers review them. This includes adding resolution descriptions, workaround suggestions, and related ticket information, so that when engineers do review the tickets, the necessary context is already prepared, significantly reducing the time needed for resolution.
Solution Approach 2:
The quality ticket system serves itself by automatically processing and enriching tickets using AI language models and vector embeddings. The system independently identifies similar tickets, extracts relevant information, and adds enrichment data without requiring manual intervention from engineering resources, enabling self-service processing that accelerates the resolution cycle.
2Reliability
If diverse descriptions of similar problems are processed manually, then nuanced understanding is possible, but duplicate reports increase and resource allocation becomes inefficient
Solution Approach 1:
The system creates vector embeddings as copies of the semantic meaning of quality ticket descriptions. These embeddings capture the essence of the problem regardless of the specific wording used by users. By comparing these embedding copies, the system can accurately identify duplicate or similar tickets even when users describe the same problem in different ways, preventing redundant resource allocation.
Solution Approach 2:
The system transforms the textual descriptions of quality tickets into vector embeddings, changing the representation from raw text to a mathematical vector format that captures semantic meaning. This parameter transformation enables the system to compare and match similar problems effectively, improving both the reliability of problem identification and the efficiency of resource allocation by reducing duplicates.
3Ease of operation
If quality tickets are enriched with additional data, then the effectiveness of issue resolution improves, but the complexity of the system increases
Solution Approach 1:
The system introduces an intermediary layer using AI language models and vector embeddings that automatically handles the complexity of data enrichment. Instead of the system directly managing complex data relationships, the language model acts as an intermediary that processes the incoming ticket, searches the knowledge base, and returns enriched data in a standardized format, simplifying the overall system architecture while improving resolution effectiveness.
Solution Approach 2:
The system implements feedback mechanisms where the enriched data from similar tickets and the knowledge base is fed back into the quality ticket record. This feedback loop continuously improves the effectiveness of issue resolution by providing engineers with relevant historical information and resolution patterns, while the system complexity is managed through automated feedback processing rather than manual curation.
Data Source
AI summary
Searches based on an incoming ticket identify quality ticket enrichment data using a vector database. Language model prompts target particular kinds of quality ticket data. The incoming quality ticket, or a search result ticket, or both, are enriched using enrichment data, such as a user intent identification, a workaround suggestion, a resolution description, a target audience description, a relevance description, an impact description, a description of missing resolution facilitation information, an association between the incoming quality ticket and the search result ticket, a user sentiment identification, a tag suggestion, or a feedback utility estimate. The enrichment reduces engineering and support burdens, and facilitates faster more effective resolution of the problem or the request that is stated or implied in the incoming quality ticket. Duplicate tickets are merged or removed. Tickets are prioritized. Missing problem resolution information is identified and requested sooner.