Structuring your GovCon’s data for AI success

Artificial intelligence (AI) has the potential to revolutionize how government contractors approach business development and proposal writing, offering tools that streamline processes, enhance efficiency, and generate high-quality content. But, as with any AI system, the success of the output depends heavily on the data fed into it. The adage “garbage in, garbage out” is particularly true in AI applications, especially in the context of federal contracting, where precision, accuracy, and compliance are critical. 

In this post, we’ll explore how contractors can structure their data for AI success, ensuring that their AI tools deliver high-quality, on-brand content that aligns with their corporate voice and values. We’ll also dive into ethical considerations around data usage and offer tips on how to smartly prompt your AI tool for the best results. 

The importance of data quantity and variety 

AI tools rely on large datasets to generate useful outputs. However, the effectiveness of an AI tool is not just about the quantity of data; it’s also about the variety and quality of that data. When training an AI model or fine-tuning it to meet your specific needs, it’s essential to provide a wide array of artifacts that reflect different aspects of your business and previous performance. 

Quantity of data: Feed the machine 

For AI to generate accurate, relevant content, it needs a significant amount of data. This includes historical documents such as past proposals, statements of work, performance reports, client feedback, and more. The more data the AI has access to, the better it will be at recognizing patterns and delivering relevant content. 

However, simply uploading vast amounts of data won’t guarantee success. It’s important to ensure the data you provide is relevant to the tasks at hand. Too much irrelevant or inconsistent data can confuse the AI, leading to lower-quality outputs. 

Variety of artifacts: A well-rounded dataset 

To get the most out of your AI tool, you need to ensure that the data you provide covers a variety of artifacts. These might include: 

• RFPs and RFIs: To understand the structure and requirements of government solicitations 

• Past performance documentation: To help the AI identify key achievements, metrics, and case studies that can be incorporated into future proposals 

• Corporate capabilities: Information about your company’s core competencies, differentiators, and success stories 

• Resumes and bios: Profiles of key personnel to demonstrate their qualifications and experience 

• Graphics and visuals: Sometimes, AI tools can analyze visuals to incorporate charts, timelines, or organizational structure into proposals 

By including a diverse range of artifacts, you help your AI tool become more versatile, allowing it to generate well-rounded content that meets the varying demands of federal proposals. 

Quality of data: Ensuring accuracy and alignment 

While quantity and variety are crucial, data quality is perhaps the most important factor in structuring your data for AI success. The AI can only work with the information you give it. If that data is inaccurate, outdated, or inconsistent, the output will be too. 

“Garbage in, garbage out” 

The phrase “garbage in, garbage out” (GIGO) succinctly explains the relationship between input and output in AI systems. If you provide poor-quality data—such as incomplete information, inconsistencies, or irrelevant documents—your AI tool will produce similarly low-quality results. In the context of proposal writing, this could lead to factual errors, unclear messaging, or even non-compliance with government requirements. 

To avoid GIGO, it’s essential to regularly review and clean your data before feeding it into the AI tool. Make sure the data is: 

• Accurate: Check for factual consistency and ensure that all performance metrics, project details, and other relevant data are up-to-date 

• Relevant: Only include documents that directly relate to the task the AI is performing, such as creating proposals or summarizing past performance 

• Well-formatted: Ensure the documents are well-organized and easy for the AI to interpret. Inconsistent formatting can confuse AI tools, leading to poorly structured outputs 

Maintaining your corporate voice 

When generating proposals, one of the challenges AI tools face is maintaining your company’s corporate voice. This refers to the tone, style, and language that reflects your brand’s identity. The key to ensuring that the AI-generated content aligns with your corporate voice is to provide it with high-quality examples of past proposals and marketing materials that represent your company’s preferred style. 

Regularly update your data to reflect your current corporate messaging and objectives. For example, if your company is shifting its focus from cost-efficiency to innovation, make sure that the AI tool is trained on documents that reflect this change in tone. This ensures that the AI-generated content will stay aligned with your organization’s evolving goals. 

Ethical considerations in data usage 

Government contractors need to be particularly vigilant about the ethical considerations of data usage when implementing AI tools. Failure to properly manage sensitive information can lead to legal risks, breaches of confidentiality, and reputational damage. 

Avoid including proprietary or classified information 

One of the biggest mistakes contractors can make is including proprietary, classified, or restricted information in the data sets they use to train AI models. This can happen unintentionally, especially if old proposals or internal documents that contain sensitive information are uploaded to the AI platform without proper review. 

Before feeding any data into an AI system, it’s critical to ensure that: 

• Proprietary information: Company secrets or intellectual property that shouldn’t be publicly available is removed 

• Classified data: Any documents that contain classified information are excluded from the AI training data to avoid compliance issues 

• Client-specific details: Scrub any client-specific details, especially those involving private or sensitive government programs, unless the AI tool has the appropriate clearance and authorization 

To minimize these risks, set up processes to review and scrub data before uploading it into the AI system. Establishing clear guidelines for what data is allowed to be included will protect your company from unintended data leaks or compliance violations. 

Excluding subcontractor information 

Another important ethical consideration is ensuring that subcontractor information is not included without permission. Government contractors often work with a variety of subcontractors who may provide proprietary methods, data, or intellectual property. Including their information in your AI system without proper consent can lead to legal issues. 

Before using any data that involves subcontractors, be sure to obtain the necessary permissions and ensure that sensitive details are redacted or anonymized. 

The importance of smart prompting (#PromptSmart!) 

Once you’ve structured your data for AI success, the next step is to interact with your AI tool in a way that maximizes its potential. This is where smart prompting comes in—a technique that involves framing your prompts in a way that yields the most useful and relevant outputs. 

Why smart prompting matters 

AI tools rely heavily on the prompts they receive to generate content. A poorly framed prompt will lead to generic or irrelevant responses, while a well-crafted prompt can produce focused, high-quality outputs. 

For example, instead of prompting your AI tool with a vague question like, “Write a proposal for the Department of Defense,” try something more specific: “Generate a technical approach for a Department of Defense RFP focusing on cybersecurity, highlighting our past performance in securing federal IT systems.” This prompt provides the AI with clear direction and ensures that the output is tailored to your specific needs. 

Tips for smart prompting 

• Be specific: Provide clear and concise prompts that guide the AI in the right direction 

• Include context: The more context you give, the better the AI will be at tailoring its response to your needs 

• Iterate: Don’t be afraid to refine and adjust your prompts if the initial output doesn’t meet your expectations. Often, it takes a few iterations to get it right 

Structuring your data properly is essential for AI success, ensuring that your tools generate high-quality, compliant content. By using diverse, high-quality data and practicing ethical data management, you can maximize the potential of your AI system. Unanet ProposalAI helps government contractors optimize their proposal processes while ensuring data security and compliance.  

To learn more, schedule a consultation with one of our experts today.