Private AI Search Using Vector Database and LLM Prompt Injection
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Solution Overview
Problem
Large language models (LLMs) trained on publicly available data may not generate accurate responses to user queries about private data, and re-training them with private data is computationally expensive and poses security risks.
Innovation Solution
A system that uses a private AI search platform to retrieve private data from a vector database, inject context into LLM prompts, and generate responses securely, reducing computational complexity and enhancing privacy and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an LLM is re-trained with private data to generate accurate responses, then response accuracy improves, but computational cost and security risks increase
Solution Approach 1:
The system segments the data handling process into two distinct parts: (1) the LLM is trained only on publicly available data, and (2) private data is stored separately in a vector database. When a query is made, the system retrieves relevant private data portions from the vector database and combines them with the LLM's public knowledge to generate accurate responses, avoiding the need to re-train the entire model with private data.
Solution Approach 2:
The patent introduces a vector database as an intermediary component between the LLM and private data. The vector database stores embeddings of private data and retrieves relevant portions based on query semantics, acting as a mediator that enables the LLM to access private information without requiring re-training, thus reducing computational cost while maintaining response accuracy.
2Measurement precision
If an LLM is re-trained with private data to generate accurate responses, then response accuracy improves, but security risks increase
Solution Approach 1:
The system segments the data handling process into two distinct parts: (1) the LLM is trained only on publicly available data, and (2) private data is stored separately in a vector database. When a query is made, the system retrieves relevant private data portions from the vector database and combines them with the LLM's public knowledge to generate accurate responses, avoiding the need to re-train the entire model with private data.
Solution Approach 2:
The patent introduces a vector database as an intermediary component between the LLM and private data. The vector database stores embeddings of private data and retrieves relevant portions based on query semantics, acting as a mediator that enables the LLM to access private information without requiring re-training, thus reducing computational cost while maintaining response accuracy.
3Use of energy by moving object
If a predefined LLM is used with publicly available data, then computational cost is reduced, but response accuracy for private data queries deteriorates
Solution Approach 1:
The system segments the data handling process into two distinct parts: (1) the LLM is trained only on publicly available data, and (2) private data is stored separately in a vector database. When a query is made, the system retrieves relevant private data portions from the vector database and combines them with the LLM's public knowledge to generate accurate responses, avoiding the need to re-train the entire model with private data.
Solution Approach 2:
The patent introduces a vector database as an intermediary component between the LLM and private data. The vector database stores embeddings of private data and retrieves relevant portions based on query semantics, acting as a mediator that enables the LLM to access private information without requiring re-training, thus reducing computational cost while maintaining response accuracy.
Data Source
AI summary
A system may receive, via an input field on a chat interface of a computing device, a user query. A system may retrieve, from a vector database, a private data portion that is responsive to the user query. A system may transmit a prompt to a large language model, where the prompt includes the user query and the private data portion. A system may receive, from the large language model, a model response with textual data that responds to the user query, where the textual data is generated by the large language model using the private data portion. A system may initiate display of the model response in the chat interface.