Specialized LLM Search Using a Closed Threat Database
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Solution Overview
Problem
Traditional natural language search tools waste processing power and computational resources due to irrelevant data searches, leading to hallucinations and inefficiencies in cloud service provider utilization, especially with data center constraints and load limitations.
Innovation Solution
The system divides search tasks into specialized database queries, limiting the search universe for large language models, reducing computational load and eliminating hallucinations by using a closed universe of files from the specialized database, and providing an interface for explainable outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional natural language search tools query large databases with vast amounts of information, then comprehensive search coverage is achieved, but processing power is wasted and hallucinations occur
Solution Approach 1:
The search system is divided into two distinct components: a traditional search engine that queries structured databases to retrieve relevant files, and a large language model that processes only those retrieved files. This segmentation allows each component to operate within its optimal domain, preventing the LLM from wasting processing power on irrelevant data while maintaining comprehensive search coverage through the traditional engine's broad database access.
Solution Approach 2:
A traditional search engine acts as an intermediary between the user's natural language query and the large language model. The search engine retrieves relevant files from the database based on the query, and these retrieved files are then fed to the LLM for processing. This intermediary prevents the LLM from directly querying the entire database, thereby eliminating hallucinations caused by irrelevant data while preserving the LLM's ability to generate accurate responses from relevant sources.
2Adaptability or versatility
If data centers process huge transfers of data, then search capacity is provided, but capacity goes unused most of the time
Solution Approach 1:
Instead of provisioning data center capacity for the maximum possible data transfer volume (excessive action), the system uses a traditional search engine to retrieve only the necessary subset of files relevant to each query (partial action). This approach provides adequate search capacity for actual user needs while avoiding the energy waste of maintaining infrastructure capable of handling much larger, unused data transfers.
3Reliability
If cloud service providers are not used for search, then load limitations are avoided, but reliability and scalability advantages are not realized
Solution Approach 1:
The system architecture is segmented into a traditional search engine component that handles database queries and file retrieval, and a large language model component that processes the retrieved files. This segmentation allows the system to leverage cloud service provider infrastructure for both components, achieving reliability and scalability advantages while managing load appropriately through the search engine's filtering capability.
4Measurement precision
If verification of large language model results is performed through further queries, then accuracy is confirmed, but massive processing power is consumed
Solution Approach 1:
The traditional search engine performs preliminary action by retrieving only the most relevant files based on the user's query before the large language model processes them. This pre-filtering ensures that the LLM receives a curated set of highly relevant files, reducing the need for verification queries while maintaining result accuracy. The preliminary retrieval action prevents the LLM from generating hallucinated responses by limiting its input to relevant sources only.
Data Source
AI summary
Systems and methods are disclosed herein for receiving, based on user interaction with a user interface, a user input of a natural language search query for identifying a cybersecurity threat by way of a search interface, the natural language search query requesting a specialized search of a threat database. An application generates a search vocabulary based on the natural language search query. The application performs a query lookup using the threat database, the query lookup returning a plurality of files that at least partially match the search query. The application prompts a large language model to generate an answer to the natural language search query using the plurality of files that at least partially match the search query, and outputs for display the answer using the user interface.