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Simple and Easy
Inspiration
What it does
How we built it
Challenges we ran into
Accomplishments that we're proud of
What we learned
What's next for Ask Solver
Inspiration I wanted to build a solution that uses the power of Ai to solve problems quickly and accurately. The idea was to create a backend service that could process queries, generate smart responses, and integrate easily with any front-end application. This inspired us to build Ask Solver, an Ai-powered assistant built using Spring Boot.
What it does Ask Solver receives a user’s question or problem, sends it to an Ai model for processing, and returns an intelligent, relevant, and structured answer. It acts like a backend brain that any application—web, mobile, or desktop can use to get Ai-generated solutions.
How we built it I built the project using: Spring Boot as the main backend framework Maven for building, managing dependencies, and structuring the project IntelliJ IDEA as the primary development platform Integration with an Ai model (LLM Api) to process user inputs and generate responses REST APIs to communicate with the frontend or external services The system is modular, scalable, and easy to extend.
Challenges we ran into Integrating Ai APIs and handling unpredictable response formats Managing Api rate limits, tokens, and latency Ensuring fast and stable response times within Spring Boot Configuring CORS and secure Api calls Handling JSON parsing and error cases in ai responses Deploying the application with environment-specific Ai keys
Accomplishments that we're proud of Successfully integrating a live Ai model inside a Spring Boot backend Building clean and reusable REST endpoints Creating a stable, production-ready backend environment Designing a system that any app can plug into to get Ai-powered answers Achieving fast response times and smooth communication between services
What we learned How to integrate modern Ai/LLM APIs into Java + Spring Boot Effective use of maven for dependency management Structuring scalable backend services Handling external API calls reliably Improving error handling, logging, and debugging Best practices in building ai-backed microservices
What's next for Ask Solver Make more secure, scalable, features Implement RAG for up to date Adding support for multiple ai models (local + cloud) Creating a full Ui for user interaction Deploying on cloud platforms like AWS, GCP, or Azure Adding conversation history, chat memory, and analytics
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