Learn how to get real results from AI in your business.

Many companies want to use artificial intelligence (AI) to improve their work and create real business value. But just adding tools like Microsoft CoPilot is not enough. The true value of generative AI (gen AI) comes when you combine the smart power of large language models (LLMs) with your own company’s information.

What Is RAG and Why Is It Important?

One of the best ways to use your company’s data with LLMs is through a method called Retrieval Augmented Generation (RAG). RAG combines your internal data with the AI model to answer questions better and faster. But for RAG to work well, the data you give it — especially unstructured data — must be good quality.

The Challenge of Unstructured Data

Most businesses already work hard to keep structured data (like numbers in databases) clean and useful. But unstructured data — such as emails, meeting notes, Word files, or SharePoint documents — is much harder to manage. This kind of data is often messy, outdated, or stored without order.

Although people tried to solve this problem in the past, the rise of gen AI has made it more important than ever. Many company leaders now say poor data quality is slowing down their AI projects.

What Does “Good Quality” Mean for Unstructured Data?

High-quality data doesn’t just happen. It takes strong leadership, clear responsibilities, and ongoing effort. Experts say that when AI fails, it’s often because the human systems behind it are broken.

In fact, 80% of the time spent on an AI project is usually focused on preparing the data. For RAG to work, the data must be:

• Relevant
• Free of duplicates
• Accurate and up-to-date
• Easy for AI to understand (with context)

Unlike structured data, unstructured documents often don’t have labels, shared definitions, or clear organisation. Also, many documents were created for other reasons — not for AI. A legal contract, for example, was not made to explain supplier risks to a robot.

A Simple Process to Improve Unstructured Data

There is no magic solution, but you can improve your data step by step. Here’s a practical approach:

1. Don’t try to fix everything at once. Start small. Focus on the most valuable problems. Begin with data that is already in decent shape.
2. Choose your data carefully. Don’t include every possible file. Pick a small group of documents and check their quality first. If the quality is too low, it may not be worth starting yet.
3. Build the right team. People who work with the data every day usually know best what “good data” looks like. Make it a team effort.
4. Prepare the data:
   • Humans: Agree on key terms and definitions. Create a business glossary. Select the best documents, tag them with metadata, and score them for quality.
   • AI tools: Use gen AI to summarise, classify, and tag documents. AI can also find and remove duplicates and build knowledge graphs. Start with humans, then use AI to scale the work.
5. Build and test your application. Use expert developers to build the RAG system. Test it with real questions and measure how well it answers. AI models change often, so keep testing regularly.
6. Keep improving over time. No system is perfect on day one. Add human oversight for important decisions, look for errors, and fix root problems. Train your staff to create better documents and store them properly.

Using RAG together with LLMs and your own data can be a powerful way to use AI in your business. Improving unstructured data is not easy, but it is possible — and very rewarding.

Want to unlock the full value of AI in your company? CREAPLUS offers expert support for AI strategy and secure implementation. Our team can guide you through data challenges, help you build high-value RAG solutions, and ensure your AI is ready for the future.

Contact CREAPLUS AI professionals today to plan your AI journey and build a secure, successful solution.