How It All Started
On November 1, 2025, I attended the AWS Student Community Day in Tirupati, and honestly, one session significantly changed my perspective on developing AI applications. Amin Ali, a Software Engineer at Target Australia and an AWS-certified Solutions Architect, delivered a presentation titled "Generative AI in Action." The session concentrated on how to build your own private GPT using AWS Bedrock.
The reality is that we've all interacted with ChatGPT, haven't we? Have you ever considered how you might develop something similar that operates with your organization's internal files, retains all the information in your own cloud, and avoids transmitting any data to external APIs? This session focused on that topic, and I'm thrilled to share my insights.
Why Build Your Own Private GPT?
You might wonder why you can't just use tools like ChatGPT or Claude directly. That's a good question, and I can explain with an example.
Data Control: Your Information, Your Rules
Imagine you run a hospital and need an AI to help doctors find patient information fast. Would you want to give private medical data to a public AI service? Probably not. By creating your own private GPT, you control your data within your AWS system, so you don't need outside services. Your private information stays safe in your Virtual Private Cloud. You'll also have full records of everything that happens, which helps follow strict rules, and no data is ever shared with anyone else.
Customization: Make It Yours
Public AI models usually learn from information found online. But what if you need answers that are only about your company? A private GPT can give answers based on your own files and knowledge, allowing it to sound like your company. It's like a customer helper who knows everything about your products. It can also connect easily with your company's internal systems, such as employee records, product lists, or instruction guides.
Understanding Generative AI
First, let's be sure we all know what generative AI is. Amin explained it well with a three-step process.
Input Processing: You tell the AI what to do, like giving it words, pictures, or code. Big AI programs like GPT, Claude, or Llama, which have learned from tons of information, handle these instructions.
Foundation Models: Regular machine learning finds patterns in data to guess things, like if an email is spam. But large language models create brand new things from nothing. That's why they are called "generative."
Creative Output: The AI makes new words, pictures, code, and other things that didn't exist before. It's like having a helpful assistant that can create, write code, and solve problems.
Why Generative AI Is Exploding Right Now
Now is a good time to learn about this new technology. Generative AI is popular for these reasons:
The AI Revolution: Many advanced AI models like ChatGPT, Claude, Mistral, and Llama 3 are becoming more common, and they are significantly altering how we work and create.
Enterprise Adoption: AWS is assisting companies in building reliable and secure generative AI systems that can manage a lot of users. Businesses don't have to build everything from scratch anymore.
Industry Impact: Smart automation is transforming industries like healthcare, shopping, schools, and banking. Think about doctors available 24/7 or educational tools that adjust to each student's needs.
Global Scale: All kinds of companies, from small new businesses to large established ones, are using generative AI a lot, and this trend is growing quickly.
Real-World Use Cases
Let's discuss practical applications. This isn't just theory these are real scenarios where private GPTs genuinely make a difference.
Customer Support: AI chatbots provide 24/7 personalized assistance that is as effective as a human helper. Imagine a customer checking your return policy late at night and getting a fast, accurate response that aligns with your actual rules.
Report Generation: Automatically generating business reports, summaries, and documents. Rather than spending a lot of time compiling quarterly reports, the AI collects the data and creates well-organized documents on its own.
Document Q&A: An internal knowledge base that enables quick answers based on company documents. Picture a new employee asking, "What is our vacation policy?" and the AI quickly finding the answer in the employee handbook.
Developer Assistants: Generating code, fixing errors, and writing explanations to speed up the development process. Programmers can ask, "How do I connect to our database?" and receive code examples that work with your system.
The Live Demo: MBU Virtual Assistant
This is where things became truly intriguing. Amin demonstrated an AI assistant he developed for Mohan Babu University.
The interface was smooth simple chat design built with React that appeared both professional and user-friendly. When a question such as "What programs does MBU offer?" was asked, the system would process the query through the chat, scan the uploaded university documents using vector similarity, generate a contextually appropriate response with AWS Bedrock, and deliver the answer in real time.
The demo showcased the chat interface with live-streaming responses and Bedrock integration performing real-time searches on the knowledge base using vector search examples. Powered by Amazon Bedrock, the responses flowed smoothly, creating the impression of conversing with an intelligent assistant.
How the Architecture Works
Let me guide you through the system's structure. Don't worry I'll keep it simple and easy to understand.
The User Journey
Here's the journey of a user's query through the system:
User Interaction: You enter a question using the React-based web interface.
API Gateway: Your request reaches AWS API Gateway, which serves as the main entry point (REST endpoint).
Lambda Function: API Gateway forwards the request to an AWS Lambda function imagine this as the central component that manages all operations.
Bedrock Integration: Lambda calls AWS Bedrock's Knowledge Base to perform a vector search and uses the Large Language Model to create the response.
Response Delivery: The answer then travels back through the same route and appears on your screen.
Architecture Components
Amin broke down the architecture into clear layers using a complete serverless setup:
This serverless configuration means you don't need to manage any servers. AWS handles scaling and security automatically.
Setting Up the Knowledge Base
This is where the real action takes place. How can you train the AI on your particular documents?
Data Preparation Pipeline
The process follows three key steps:
Upload PDFs: Your documents (manuals, policies, guides) are uploaded to an S3 source bucket.
Extract & Chunk: The system pulls out the text and divides it into smart segments for the best vector embeddings.
Generate Embeddings: AWS Bedrock generates vector embeddings that are saved in an S3 vector bucket for similarity searches.
Vector Integration
Does this ring a bell? Imagine vector embeddings as a library catalog. Instead of just matching exact words, vectors understand the meaning behind them.
The Bedrock Knowledge Base connects to an S3 vector bucket with 2024-dimensional embeddings. When you ask a question, the system finds relevant sections of documents using cosine similarity, searches for matching info in real time, and provides answers based on the most important details.
The setup involves connecting to an S3 vector bucket, automatically updating indexes, and using Bedrock directly – all these components work together seamlessly.
Query Handling Flow
Let's trace a question through the whole system.
Step 1 - User Query: You type a question into the React chat interface, just like you would in any messaging app.
Step 2 - Vector Search: Lambda checks the Bedrock Knowledge Base for relevant document sections. The system finds the 3-5 most relevant pieces of information from your document collection.
Step 3 - Context Response: The Large Language Model creates a response based on the retrieved context and sends it back to the user interface. The answer is based on your actual documents, not general internet information.
Best Practices and Optimization
Building the system is one thing, but making it run well is different. Amin shared important tips for improvement.
Prompt Engineering
Writing good prompts helps get better and more useful answers.
Use clear, specific instructions. Instead of saying "tell me about products," say "list the top 3 features of Product X based on our product documentation."
Add context and examples to your prompts.
Improve your prompts based on the responses you get, prompt engineering is a process that requires adjustments.
Performance Optimization
Speed and cost are important in production
Allow real-time responses so users can see answers as they come in, which improves the user experience.
Use tokens wisely, keep prompts short since tokens cost money.
Set up caching for frequently asked questions.
Key Takeaways
After witnessing this in action, three key points caught my attention:
AWS Bedrock: Secure and Flexible: AWS Bedrock provides a secure and flexible foundation for developing private AI solutions. You receive strong security features without needing to build everything yourself.
Production Ready: Generative AI is now ready for business use and has demonstrated real results that justify the investment. This is no longer just experimentation – companies are implementing these systems in their daily operations.
Easy Implementation: Building a private GPT is simpler than ever with managed services. You don't need to be a machine learning expert to get started.
Conclusion
After leaving that session, I realized that AI isn't just about using ChatGPT. It's about building smart systems tailored to your specific needs while keeping your information secure.
The AWS Student Community Day in Tirupati had many great sessions, but this one impressed me because it demonstrated how to turn an idea into a working demo. Whether you're making a company assistant, a customer support bot, or a documentation helper, the architecture Amin presented provides a solid foundation.
In summary, if you're thinking about adding AI features to your apps, AWS Bedrock makes it simpler than you might expect. With managed services, strong security, and flexibility, you can focus on solving your specific problem instead of building the entire AI setup yourself.
I hope this explanation helps you understand how private GPTs work and inspires you to create your own. The future of AI will be private, secure, and tailored to your personal needs.
About the Author
_As an AWS Community Builder, I enjoy sharing the things I've learned through my own experiences and events, and I like to help others on their path. If you found this helpful or have any questions, don't hesitate to get in touch! 🚀
🔗 Connect with me on LinkedIn_
References
Event: AWS Student Community Day Tirupati
Topic: How AWS Lambda and Fargate Change the Way We Build Apps
Date: November 01, 2025
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