Opt for purpose-built LLMs over generic ones for your govt. proposals

Artificial intelligence, particularly generative AI, has become a powerful tool for enhancing proposal quality and efficiency in government contracting. However, as organizations face budget and time constraints, some are turning to public large language models (LLM) like Chat GPT-4, Claude, and Gemini to rather than investing in specialized solutions.  

This approach raises important security concerns and fails to capitalize on the full potential of purpose-built AI tailored specifically for government contracting. Here’s why.  

Privacy and security 

Public AI models store data on shared servers, creating risk for government contractors who handle sensitive data and competitive information.  

Even proposals that do not contain CUI can contain sensitive trade secrets. When proposal data, pricing strategies, or past performance details are uploaded to public AI platforms, that data can become part of the model’s training data – potentially exposing proprietary information to competitors.  

In contrast, purpose-built models use private and secure databases with restricted access, isolating your data from other users.  

Compliance 

Purpose-built AI models for government contracting are designed to handle the complexities of federal contracting. These tools understand Federal Acquisition Regulations (FAR), Defense Federal Acquisition Regulation Supplements (DFARS), and other crucial compliance requirements. They can automatically ensure that proposals meet solicitation requirements and include necessary clauses – capabilities that generic AI models simply don’t have.  

Purpose-built models can also integrate with proposal workflows, automatically generating compliance matrices and performing systematic compliance checks. Generic models lack this specialized knowledge.  

Performance  

Public models are trained on large amounts of information that is not specific to a business or topic. Although ChatGPT Plus users can upload documents to the model, they cannot use those documents for future use or use those documents in aggregate (knowledge repository) to tailor responses. Claude users can take advantage of a private workspace where they can establish contracts as projects and tailor proposal responses with previous proposal/contract data. Where Claude falls short, however, is it has not been trained on federal acquisition processes, resulting in tailored responses and workflows that take into account regulations like FAR.  

Generic AI models offer impressive general capabilities, having been trained on massive amounts of information – but they lack the deep domain expertise needed for government contracting. Purpose-built solutions are trained on relevant government contracting data including federal procurement regulations and policies, past performance documentation, and historical proposal data and win themes.  

This specialized training allows them to generate more accurate, relevant, and compliant proposal content that aligns with federal regulation criteria.  

Custom training 

While some public models like ChatGPT Plus offer limited document upload features, they can’t provide continuous learning or leverage those documents for future proposals.  

Purpose-built AI solutions can be trained securely on your organization’s past proposals, win themes, and differentiators. This creates a knowledge repository that grows more valuable over time. 

For AI proposal generation, purpose-built beats generic every time 

Public AI models may seem like a great option because of accessibility and low cost. But they present significant risks and limitations for government contractors.  

Purpose-built AI solutions provide the security, compliance capabilities, and domain expertise that generic models can’t match. They deliver the specialized features needed to success in the competitive federal marketplace.  

Looking to make business easy while freeing up more time to do the work that matters? Learn how Unanet can help.  Schedule a demo today.