Data Structuring Enhancement with LLM
Budget: $30 – $250 USD
Improvement of AI Learning Module for Enhanced PDF Data Structuring
1. Introduction
We are seeking proposals from qualified vendors to develop an AI learning module that enhances the structuring of information extracted from PDF files. The module will utilize GPT-4o and employ a series of prompts to emulate the learning process, incorporating user-curated examples to improve output accuracy over time.
2. Project Overview
The goal of this project is to build an AI learning module capable of refining its data structuring accuracy through one or two-shot learning. Initially, the module may structure the data incorrectly, but with user-provided valid examples, it should learn and improve its accuracy in subsequent analyses.
3. Project Scope
The proposed solution should include the following:
1. Initial Analysis and Structuring:
o The AI module should analyze and structure the information from a PDF file based on the current prompt.
o Identify common errors or inaccuracies in the initial structuring.
2. One or Two-Shot Learning:
o Implement a mechanism to inject user-curated valid examples into the prompt.
o Utilize these examples to refine and improve the AI's structuring accuracy in subsequent analyses.
o A learning iteration can be composed of adding, editing or removing KV pairs or table columns and/or data.
3. Prompt Series Development:
o Design a series of prompts that simulate the learning process, incorporating user feedback effectively.
o Ensure the prompts are adaptable to various types of PDF documents and data structures.
o A learning iteration can be composed of adding, editing or removing KV pairs or table columns and/or data.
4. Output Validation:
o Validate the improved structuring against predefined accuracy benchmarks.
o Provide a feedback loop for continuous learning and improvement.
4. Submission Requirements
Proposals should include the following information:
1. Vendor Information:
o Company name, address, and contact details.
o Brief company overview and relevant experience.
2. Technical Approach:
o Detailed description of the proposed solution, including methodologies and technologies to be used.
o Description of the prompt series and learning mechanism.
o Approach to integrating user-curated examples into the learning process.
o Be sure to include examples to demonstrate how your prompts are working correctly on forms that you already have at hand.
5. Evaluation Criteria
Proposals will be evaluated based on the following criteria:
1. Technical approach and methodology.
2. Experience and past performance.
3. Quality of the proposed solution.
We look forward to receiving your proposal and thank you for your interest in this innovative project.
Note: We have attached a sample series of prompts with a sample PDF that we are using which injects an example, but this series of prompts is not sufficient to simulate a learning experience
1. Introduction
We are seeking proposals from qualified vendors to develop an AI learning module that enhances the structuring of information extracted from PDF files. The module will utilize GPT-4o and employ a series of prompts to emulate the learning process, incorporating user-curated examples to improve output accuracy over time.
2. Project Overview
The goal of this project is to build an AI learning module capable of refining its data structuring accuracy through one or two-shot learning. Initially, the module may structure the data incorrectly, but with user-provided valid examples, it should learn and improve its accuracy in subsequent analyses.
3. Project Scope
The proposed solution should include the following:
1. Initial Analysis and Structuring:
o The AI module should analyze and structure the information from a PDF file based on the current prompt.
o Identify common errors or inaccuracies in the initial structuring.
2. One or Two-Shot Learning:
o Implement a mechanism to inject user-curated valid examples into the prompt.
o Utilize these examples to refine and improve the AI's structuring accuracy in subsequent analyses.
o A learning iteration can be composed of adding, editing or removing KV pairs or table columns and/or data.
3. Prompt Series Development:
o Design a series of prompts that simulate the learning process, incorporating user feedback effectively.
o Ensure the prompts are adaptable to various types of PDF documents and data structures.
o A learning iteration can be composed of adding, editing or removing KV pairs or table columns and/or data.
4. Output Validation:
o Validate the improved structuring against predefined accuracy benchmarks.
o Provide a feedback loop for continuous learning and improvement.
4. Submission Requirements
Proposals should include the following information:
1. Vendor Information:
o Company name, address, and contact details.
o Brief company overview and relevant experience.
2. Technical Approach:
o Detailed description of the proposed solution, including methodologies and technologies to be used.
o Description of the prompt series and learning mechanism.
o Approach to integrating user-curated examples into the learning process.
o Be sure to include examples to demonstrate how your prompts are working correctly on forms that you already have at hand.
5. Evaluation Criteria
Proposals will be evaluated based on the following criteria:
1. Technical approach and methodology.
2. Experience and past performance.
3. Quality of the proposed solution.
We look forward to receiving your proposal and thank you for your interest in this innovative project.
Note: We have attached a sample series of prompts with a sample PDF that we are using which injects an example, but this series of prompts is not sufficient to simulate a learning experience