Food Delivery AI Assistant for Context-Aware Query Handling
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Solution Overview
Problem
Existing food delivery systems struggle to accurately respond to user queries in natural language, particularly those that do not fit into traditional search formats, leading to inefficiencies and increased user input requirements.
Innovation Solution
Implementing a machine-learned large language model (LLM) within a food delivery system to process natural language queries, generate contextual responses, and provide selectable options in a carousel format, while also proactively suggesting items based on user data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search features are used to handle user queries, then the system structure remains simple, but the system cannot accurately respond to non-standard natural language queries
Solution Approach 1:
A large language model is introduced as an intermediary component between the user interface and the food delivery system. This mediator processes natural language queries, extracts relevant parameters (such as cuisine type, price range, delivery time), and translates them into structured search criteria that the existing system can handle, thereby enabling accurate response to non-standard queries without completely redesigning the system architecture
Solution Approach 2:
The query processing function is segmented into distinct components: natural language input reception, contextual data retrieval, model input generation, LLM processing, and result presentation. This segmentation allows the complex task of handling natural language queries to be broken down into manageable steps, improving response accuracy while maintaining system organization
2Measurement precision
If the system requests more user input to clarify queries, then query understanding improves, but user input requirements and interaction time increase
Solution Approach 1:
The system performs preliminary actions by retrieving contextual data (such as user preferences, order history, and current system state) before processing the query. This pre-fetching of relevant information allows the large language model to generate accurate responses with minimal additional user input, reducing interaction time while maintaining query understanding accuracy
Solution Approach 2:
The system utilizes feedback from contextual data and previous interactions to inform query processing. By analyzing user preferences, past orders, and current system state, the system can infer intent from brief queries and provide accurate responses without requiring extensive user clarification, thereby reducing interaction time
3Measurement precision
If a machine-learned large language model is integrated to process natural language queries, then response accuracy to nonstandard queries improves, but processing resources and system complexity increase
Solution Approach 1:
The system applies partial action by using the large language model only for the specific task of processing natural language queries, rather than applying it to all system operations. For routine tasks and standard queries, the existing efficient processing pathways are used, thereby reducing overall processing resource consumption while maintaining high response accuracy for natural language inputs
Data Source
AI summary
Systems and methods for providing an AI assistant to users of a food delivery system. The method includes receiving a user query, wherein the user query is associated with a food delivery system. The method further includes accessing contextual data for the user query. The method further includes generating model input, the model input including the user query and the contextual data for the user query. The method further includes providing model input as input to a machine-learned large language model. The method further includes receiving a query response as an output of the machine-learned large language model processing the model input. The method further includes outputting the query response to the user for display, the query response comprising a carousel of selectable options available through the food delivery system.