AI RFP Response Using Data Chunking, Embeddings, and Lineage
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Solution Overview
Problem
Existing RFP response processes are time-consuming and resource-intensive due to manual data gathering and lack of sophisticated AI-based automation, particularly in handling complex data import and generating contextually appropriate answers.
Innovation Solution
A computer-implemented method utilizing Large Language Models (LLMs) for automated RFP response, involving data import, chunking, context string embedding, and metadata formation to optimize data storage and retrieval, enabling AI-based answer generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data gathering and analysis methods are used for RFP responses, then data accuracy can be maintained through human review, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The system performs preliminary data processing by chunking customer data and generating embeddings before RFP responses are needed. This advance preparation allows the AI model to quickly retrieve and utilize relevant information during actual RFP responses, reducing response time while maintaining accuracy through pre-validated data structures.
Solution Approach 2:
The patent introduces an AI language model as an intermediary between raw customer data and RFP responses. The model processes and synthesizes information from embedded data chunks, enabling automated generation of accurate responses without requiring manual human review for each individual response, thus reducing time loss while preserving data accuracy.
2Extent of automation
If traditional software platforms are used to streamline RFP processes, then some automation is achieved, but the systems lack sophisticated AI-based answer generation capabilities
Solution Approach 1:
The system transforms customer data into a different parameter representation by converting raw text into embedded vectors. This parameter change enables the AI model to process and generate answers more effectively, combining high-level automation with sophisticated answer generation capabilities that traditional platforms lack.
3Measurement precision
If comprehensive customer data is imported and processed into chunks with embeddings, then AI-based answer generation accuracy is improved, but data management complexity increases
Solution Approach 1:
The patent segments customer data into manageable chunks and creates embeddings for each segment. This segmentation strategy enables the AI model to process large volumes of data efficiently while maintaining answer generation accuracy. The segmented approach actually reduces management complexity by organizing data into standardized, queryable units rather than handling monolithic data structures.
Data Source
AI summary
A computer implemented method includes importing customer data including previously completed requests for proposals, product documentation, sales materials, security policies and competitor documentation. The customer data is analyzed to form categories. The customer data is processed into chunks of characters to form customer data chunks. The customer data chunks are embedded into context strings. Metadata characterizing customer data lineage is formed. The customer data chunks, the context strings and the metadata are stored in a database.