Build Your Own AI for Fun or Profit 

By now, you’ve likely encountered artificial intelligence (AI) in your daily life. Whether it’s through OpenAI’s ChatGPT, Google’s Gemini, Elon Musk’s Grok, Meta’s Llama, or other tools, AI is becoming a vital part of how we work, create, and interact with technology. If you’re still just “dabbling” in AI, that’s fine—but understanding and integrating it into your life could be key to your future success, both professionally and personally. 

For everyday tasks like writing emails, generating ideas, or even creating graphics and videos, third-party AI tools are fantastic as long as your work doesn’t involve sensitive data. But what if you want to take AI to the next level? What if you want an AI that understands your world—your job, your sector, or the specific data you rely on every day? 

That’s where Retrieval-Augmented Generation (RAG) comes in. 

What is RAG? 

RAG, or Retrieval-Augmented Generation, is a technology that combines large language models (LLMs) with external retrieval systems. Think of it as giving an AI extra context—making it a supercharged assistant tailored to your specific needs. 

Imagine this: 

• You upload your data: Business documents, customer FAQs, product manuals, or research files. 

• RAG combines this data with AI: It delivers responses that feel like ChatGPT but are tailored to your specific context. 

• You get real references: Unlike some AI models, RAG can show exactly where its answers came from, making it invaluable for legal documents, bibliographies, or other critical uses. 

What Can RAG Do for You? 

RAG isn’t perfect for everything (more on that later), but it excels in: 

• FAQ Agents: Building an AI-powered agent that can instantly answer customer or team questions based on your internal knowledge base. 

• Document Search: Upload multiple documents and ask the AI to find relevant answers, saving hours of manual work. 

• Organizational Insights: Make your AI understand your company’s policies, procedures, and processes, giving you faster, smarter decision-making tools. 

Pro Tip: AI, including RAG, struggles with numbers. While it’s great at summarizing text or analyzing trends, it’s not ideal for financial forecasting, time-sensitive predictions, or interpreting spreadsheets. Use it for language-based questions and tasks, not number-heavy calculations. 

When to Be Cautious 

If you’re working with sensitive data like personally identifiable information (PII), protected health information (PHI), intellectual property (IP), or trade secrets, it’s critical to use AI responsibly. Here’s how: 

1. Use a Secure Cloud

Consider cloud platforms like Azure, AWS, or Google Cloud, which offer secure AI environments. However, don’t just rely on their assurances—add your own layers of protection. For example: 

• Guardrails: Tools that monitor and control what the AI accesses and outputs. 

• Terms & Conditions: Always read (and save) the fine print. Tech giants are known for writing user agreements that favor them, not you. 

2. Go On-Premise

For maximum control, deploy AI models on your own servers or personal computers. This method is perfect for learning AI technology, experimenting without risk, and ensuring your sensitive data stays private. Just double-check that the local model isn’t secretly training on external cloud systems. 

3. Hybrid Deployment

A hybrid approach combines the flexibility of cloud-based systems with the control of on-premise setups. For example, you might use a cloud service for general AI tasks and a secure, private model for handling sensitive data. 

Why Should You Care About AI? 

Whether you’re an executive strategizing about sensitive data, a small business owner looking to streamline operations, or just an everyday user curious about technology, AI is no longer optional. It’s a tool that can give you a competitive edge—if you know how to use it. 

RAG and similar systems let you build AI that’s not just generic but yours. It can understand your world, adapt to your needs, and provide the insights that matter most to you. 

Getting Started with AI 

If you’re ready to dive in, here are some beginner-friendly steps: 

• Experiment with Existing Tools: Start with free or low-cost versions of ChatGPT, Gemini, or other platforms to understand AI basics. 

• Explore RAG: Use tools like LangChain or Haystack to experiment with custom AI setups. 

• Learn the Basics of AI Deployment: Familiarize yourself with terms like “on-premise,” “guardrails,” and “zero-trust.” 

• Think About Data Security: Start small, but always consider how you’ll protect sensitive information as you scale up. 

AI isn’t just a tool for tech companies or scientists—it’s for everyone. Whether you want to save time, enhance your business, or simply learn something new, now is the perfect time to start building your own AI for fun or profit. 

About the Author
Waylon Krush is the CEO of ZeroTrusted.ai, a leading provider of secure AI solutions. With over 25 years of cybersecurity and AI expertise, Waylon is passionate about helping individuals and organizations safely harness the power of AI. For more information, visit zerotrusted.ai.