Domain-Specific Vector Embeddings for LLM Query Classification
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Solution Overview
Problem
Current Large Language Models (LLMs) face challenges in providing optimal results for domain-specific queries due to a one-size-fits-all approach, limited specialized knowledge, and difficulties in integrating post-training information, leading to suboptimal responses that may lack relevance or be outdated. Additionally, the computational cost of querying LLMs is high, requiring extensive processing power and time.
Innovation Solution
The proposed method, Retrieval Augmentation Generation (RAG), enhances LLMs by incorporating a retrieval-based approach. This involves classifying queries into specific domains, retrieving domain-specific vector embeddings, and using these embeddings to prompt the LLM, thereby grounding responses in relevant index results. This approach improves response accuracy, context awareness, and computational efficiency by focusing LLM resources on pertinent information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a one-size-fits-all approach is used for query understanding and response generation, then the system is simple to implement, but the response accuracy and relevance for domain-specific queries deteriorates
Solution Approach 1:
The patent segments the knowledge base into multiple domain-specific indexes (e.g., medical, legal, technical domains) and classifies queries into corresponding domains before retrieval. This segmentation allows the system to provide domain-specific responses while maintaining a unified architecture, resolving the contradiction between simplicity and accuracy.
Solution Approach 2:
The system applies local quality by retrieving and utilizing domain-specific vector embeddings tailored to the classified query domain. Instead of using a single generic knowledge base, each domain receives customized embeddings that enhance response relevance and accuracy for that specific domain while the overall system remains coherent.
2Reliability
If domain-specific vector embeddings are retrieved and used to prompt the LLM, then the response relevance and context awareness are improved, but the device complexity increases
Solution Approach 1:
The patent introduces an intermediary retrieval layer that sits between the query classification and the LLM prompting stages. This intermediary component handles the complex tasks of domain classification and vector embedding retrieval, shielding the LLM from direct complexity while enhancing response relevance through domain-specific context.
Solution Approach 2:
The system performs preliminary action by classifying queries into domains and retrieving relevant vector embeddings before prompting the LLM. This pre-processing step prepares domain-specific context in advance, allowing the LLM to focus on generating relevant responses without being overwhelmed by the complexity of the entire system architecture.
3Measurement precision
If extensive processing power and time are allocated to ensure high accuracy, then the response quality improves, but the computational cost increases
Solution Approach 1:
The patent extracts only the necessary domain-specific vector embeddings relevant to the classified query domain and incorporates them into the LLM prompt. This extraction approach ensures that the LLM processes only essential information rather than overwhelming amounts of data, achieving high accuracy while reducing unnecessary computational overhead.
Solution Approach 2:
The system applies partial action by retrieving and processing only the top-relevant domain-specific embeddings rather than all available data. This selective approach provides sufficient context for accurate responses without the computational burden of processing every possible data point, optimizing the balance between accuracy and computational cost.
Data Source
AI summary
Aspects of the invention provide a method, computer system, and computer program product for retrieval augmented generation. In one aspect, the method includes receiving a query. The method further includes classifying the query to a first domain within a multitude of domains. The method additionally includes retrieving an index of domain-specific vector embeddings corresponding to the domains. The method further includes prompting a Large Language Model (LLM) with the query and the domain-specific vector embeddings. The method also includes receiving a query response from the LLM as grounded with the most relevant index results. The method further includes forwarding the query response.