Harnessing the power of generative AI for creating rubrics for scoring proposals

Edward Wong is a Consultant at Ibbaka. See his LinkedIn profile.

In an era where new AI technologies are emerging daily, it seems as if every industry has found a way to leverage AI. One significant area benefiting from these innovations is the evaluation of proposals. Whether for academic research, grant funding, or business ventures, the need to assess proposals efficiently and accurately is critical. 

As highlighted in our previous post Your next proposal will be evaluated by an AI, the traditional methods of proposal evaluation often fall short due to information overload, stakeholder biases, and the complexity of decision-making processes. Generative AI offers a solution to these challenges by providing a structured, objective approach to creating and applying evaluation rubrics for proposal evaluation. In this blog post, we will delve into how generative AI is revolutionizing this process and provide practical steps for implementation.

The Role of Rubrics in Proposal Scoring

Rubrics are essential tools for evaluation. They provide a standardized framework for assessing various aspects of a proposal, ensuring consistency and fairness in the scoring process. A well-designed rubric outlines specific criteria and assigns weight to each, enabling evaluators to systematically score proposals based on predefined standards. However, creating effective rubrics can be time-consuming and requires a deep understanding of the subject matter.

Generative AI is transforming rubric creation and proposal evaluation

The challenge of creating rubrics for generative AI proposal evaluation is best solved using generative AI! Here are the five steps I have been using as I develop rubrics.

1. Rapid Development of Comprehensive Criteria
   AI can quickly analyze vast amounts of data from previous successful proposals, industry standards, and organizational goals to generate a comprehensive list of evaluation criteria.

2. Customization and Flexibility
   While AI provides a strong foundation, it also allows for easy customization. Evaluators can fine-tune the AI-generated rubrics to align with specific project requirements or organizational values.

3. Consistency Across Evaluations
   AI-generated rubrics help maintain consistency in scoring across different proposals and evaluators, ensuring fairness and comparability in the selection process.

4. Providing Feedback
   In addition to scoring, AI can generate detailed feedback for each proposal, highlighting strengths and areas for improvement. This feedback can be invaluable for applicants looking to refine their proposals.

5. Dynamic Adaptation
   By analyzing scores and feedback across multiple proposals, the AI system learns from each evaluation cycle. It can identify common patterns and trends to continuously refine and improve the rubrics, adapting to evolving industry trends and organizational needs.

Implementing Generative AI for Proposal Rubric Creation and Evaluation

To harness the power of generative AI, follow these steps.

• Define Your Objective

  • Start by clearly defining what you want to achieve with your rubrics. Answer the following:

  • What are the key criteria for evaluation? 

  • What weight should each criterion carry? 

  • What specific types of proposals will you be evaluating?

• Prepare the Data
  Collect a dataset of past proposals and their outcomes. Ensure that the data is clean and well-organized. This dataset will serve as the foundation for training your AI model.

• Choose the Right Tool
  There are various AI tools and platforms available for generative AI. Choose one that suits your needs and has the capability to analyze data and generate rubrics. Some popular options include Perplexity, OpenAI’s GPT, and Google’s AI tools. It is important to test against more than one LLM (Large Language Model) and compare the results.

• Train Your Model
  Use your dataset to train the AI model. This involves feeding the data into the model and allowing it to identify patterns and generate rubrics. The training process may require some fine-tuning to ensure accuracy.

• Test and Refine
  Once your model is trained, test it with a sample set of proposals. Evaluate the generated rubrics and the AI’s scoring against human evaluations to ensure accuracy and consistency. Refine the model as needed based on these tests.

• Implement and Monitor
  After refining the model, implement it into your evaluation process. Continuously monitor the AI’s performance and gather feedback from evaluators to ensure it meets your objectives. Make adjustments as needed to improve accuracy and effectiveness.

Basic Architecture: A GAN-Inspired Approach to Proposal Evaluation

This AI-powered system for creating and evaluating rubrics draws inspiration from the structure of Generative Adversarial Networks (GANs). While not a true GAN, this approach leverages a similar adversarial dynamic to refine and improve the evaluation process. Let's break down the architecture:

Two Applications, One Shared Rubric

The key to this approach is to connect the two applications (Rubric Generator and Proposal Evaluator) with a shared document, the rubric, that connects and aligns them. This design pattern has general applicability that Ibbaka is exploring in other applications as well.

1. Rubric Generator (analogous to the GAN's Generator): Creates and refines evaluation rubrics based on historical data, project requirements, and organizational goals.

2. Proposal Evaluator (analogous to the GAN's Discriminator): Uses the generated rubric to assess and score incoming proposals.

3. Shared Rubric (the medium of interaction): Acts as the interface between the generator and evaluator.

The Adversarial Dynamic

Unlike a traditional GAN where the generator tries to fool the discriminator, our system's components work cooperatively but with competing objectives:

• The Rubric Generator aims to create the most comprehensive and discerning rubric possible.

• The Proposal Evaluator strives to apply the rubric rigorously, identifying any shortcomings or ambiguities.

This tension drives continuous improvement:

1. The Generator creates an initial rubric.

2. The Evaluator applies it to real proposals, identifying strengths and weaknesses.

3. Feedback from the evaluation process informs the Generator's next iteration.

4. The cycle repeats, progressively refining the rubric's effectiveness.

By mimicking the adversarial nature of GANs, our system ensures that both rubric creation and proposal evaluation are constantly evolving and improving. This dynamic architecture allows for rapid adaptation to changing project requirements and emerging evaluation criteria, ensuring that proposal assessment remains cutting-edge and effective.

Embracing AI for Enhanced Proposal Evaluation

The integration of generative AI in proposal evaluation marks a significant leap forward in decision-making processes. This GAN-inspired approach, combining AI-driven proposal rubric creation and evaluation, addresses longstanding challenges of inconsistency, bias, and information overload. This system not only streamlines the evaluation process but also continuously improves itself, adapting to evolving requirements and emerging criteria.

However, it's crucial to remember that AI augments rather than replaces human expertise. The human-in-the-loop element remains vital for capturing nuances that AI might miss. As we implement these systems, vigilant monitoring of performance and ethical implications is essential.

Looking ahead, the applications of this technology span various sectors, from academic grant reviews to corporate RFP processes. Organizations that effectively implement AI-driven rubric creation and evaluation will gain a significant competitive edge, better positioned to identify groundbreaking proposals and drive innovation.

The future of proposal evaluation is here, powered by AI. Are you ready to harness its potential?