LLM Query Routing With Schema Prompts to Prevent Hallucinations
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Solution Overview
Problem
Existing systems face challenges in effectively handling ambiguous natural language queries due to hard-coded communication methods between services, leading to improper responses and restricted user interactions, which are time-consuming and impractical to code for all potential use cases.
Innovation Solution
A routing service utilizes a large language model (LLM) to translate user queries into proper requests, routing them to the correct backend service, and employs a request processor to generate prompts for the LLM to ensure accurate responses, preventing hallucinations by using whitelisted rules and schema-based prompts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If hard coding is used to communicate between services, then the system structure is simple, but the system cannot handle ambiguous natural language queries and has restricted user options
Solution Approach 1:
An LLM-based intermediary service is introduced between the user and the backend services. This intermediary translates ambiguous natural language queries into structured requests that backend services can process, enabling the system to handle diverse and ambiguous queries without requiring complex hard-coded logic in each service.
Solution Approach 2:
The LLM-based routing service serves multiple functions: it acts as a translator for natural language, a router to direct queries to appropriate backend services, and a modifier to adjust queries before processing. This multi-functional approach consolidates complexity into a single versatile component rather than distributing it across multiple services.
2Productivity
If hard coding is used for all potential user requests, then the system is easy to implement, but it is time consuming and impractical to code for all use cases
Solution Approach 1:
The LLM-based routing service autonomously analyzes incoming natural language queries, determines the appropriate backend service, and routes requests without requiring pre-programmed logic for each possible query type. This self-service capability eliminates the need to manually code every potential use case while maintaining high processing speed.
Solution Approach 2:
The system changes the parameter of query processing from fixed hard-coded patterns to dynamic LLM-based interpretation. This allows the system to adapt to new query types without modifying the underlying code structure, significantly reducing maintenance time while preserving fast response times for all query varieties.
3Ease of operation
If hard coding is used to restrict user interactions, then the system is easier to control, but user options are restricted and interactions become less flexible
Solution Approach 1:
The system transitions from static hard-coded interaction patterns to dynamic LLM-based query interpretation. Users can interact with the system using natural language in diverse ways, and the LLM adaptively routes these interactions to appropriate services, providing flexibility while maintaining reliability through structured processing of user intent.
4Measurement precision
If the LLM generates responses without modification, then the system is simpler to operate, but the LLM may produce hallucinations and inaccurate responses
Solution Approach 1:
A query modification service acts as an intermediary between the user's natural language query and the LLM. This intermediary translates and structures the query before it reaches the LLM, reducing the likelihood of hallucinations by providing the LLM with clearer, more structured input while maintaining system simplicity through automated translation.
Data Source
AI summary
A user query that includes a natural language description is received. It is determined that the user query is an improper request for information. In response to a determination that the user query is the improper request for information, a proper request for information is generated.