Publishing a NuGet package has always been on my developer bucket list, but until recently, I’d never found the right project to share with the world. That changed when I started building a small utility to handle GPT calls—something I’d been re-implementing in multiple .NET projects. One day, it hit me: if this code saves me time, why not polish it up and publish it so other C# developers can benefit too?
And so, the AI Helper Library was born. Over the last few weeks, I turned what began as a scrappy internal tool into a fully packaged, documented, and tested library. Below is the story of how it all came together—my reasoning, the hurdles I faced, and the sweet relief of finally hitting dotnet nuget push.
Inspiration: Chasing Time-Saving Code
In my work—ranging from building Blazor UIs to designing HPC cluster demos—I often find myself integrating AI features for everything from code suggestions to chatbots. Each time, I ended up writing (and rewriting) the same logic: handling prompts, managing token limits, dealing with timeouts. Eventually, I started questioning why I was duplicating so much code. That’s when I realized I could centralize everything under one library.
I wanted the final result to be straightforward. The developer in me didn’t want to read 100 pages of docs just to get a simple GPT response. So, I made sure the library required minimal configuration while remaining flexible for more advanced use cases. This meant focusing on clean architecture, sensible defaults, and robust error handling.
Early Iterations: From Console App to Library
At first, I kept everything inside a simple console app. I would pass prompts to GPT-3.5 or GPT-4, parse the responses, and tinker with the logic until I liked how it behaved. Once I decided this could be bigger than a local script, I refactored it into a dedicated project. Creating a separate class library forced me to think about the public API: which methods did I really want to expose, and how would users configure them?
One key decision was using an extensible configuration system (AIExtensionHelperConfiguration) so developers could easily set things like retry counts, proxy URLs, or default instructions for the AI. This pattern ended up being a lifesaver; when I eventually added features like multi-turn conversations, I could just slot them into the existing config structure without breaking older code.
Tackling Multi-Turn Conversations
I knew from personal experience that single prompts only go so far. Real-world AI projects often require ongoing chat contexts—like a Q&A bot that “remembers” what you asked two questions ago. So I built in multi-turn conversation support, letting developers store previous messages and maintain context for each user session. The biggest challenge here was ensuring I didn’t blow through token limits or jam everything into memory if someone decided to have a marathon chat session with GPT-4.
To handle this, I included a simple mechanism to trim older messages when the conversation hits a certain length. It’s not perfect for every scenario, but it covers most common use cases without overcomplicating the code.
Retry Logic and Proxy Support
Working in enterprise environments taught me that network failures and proxies are a fact of life. So, I introduced configurable retry logic with exponential backoff—helpful for those random 503 errors or rate-limit issues. I also implemented optional proxy settings, so if you’re behind a corporate proxy, you can still connect to the OpenAI endpoints without wrestling with custom code. Those might sound like minor details, but they end up being critical in real production settings.
Documentation: A Window into the Code
While the code was fun to write, documentation was a different beast. I realized quickly that how I name methods or structure the classes can make or break the user experience. I spent a fair amount of time in the README explaining each configuration field, demonstrating sample calls, and outlining edge cases. In the process, I found and fixed small inconsistencies—like mismatched method names and optional parameters that weren’t actually optional. Writing docs is a great way to see your code through a fresh lens.
Ultimately, I created a fairly detailed README plus a console demo to showcase real usage scenarios. My hope is that someone new to the AI Helper Library can get a chatbot working without needing to read the entire source code.
Publishing to NuGet: Final Steps
The final stretch involved tidying up my .csproj file, settling on version numbers, and ensuring everything was licensed appropriately. There’s a sense of vulnerability in uploading a package for the world to see—what if there’s a hidden bug or a glaring oversight? But I reminded myself that no library is perfect from day one, and incremental improvements based on real user feedback are part of the process.
Typing dotnet nuget push and seeing the AI Helper Library appear on the official feed felt like a major milestone. It was also a reminder that now it’s out there for anyone to install, critique, or (hopefully) find helpful. If you’re curious, you can find it here: AI Helper Library on NuGet.
Lessons Learned
Focus on Configuration: Making the library flexible via a single configuration class saved me from a thousand if-else statements later on.
Document as You Go: Writing docs early helped me refine the code’s public interface and catch inconsistencies before they spread.
Plan for Real-World Edge Cases: Handling proxies, network errors, and multi-turn conversations isn’t glamorous, but it’s key to delivering a dependable library.
Iteration Over Perfection: Accept that version 1.0 is just the start. Embrace the iterative process and keep improving based on user feedback.
Wrapping Up
Looking back, this project was far more than a weekend experiment. It forced me to think about maintainability, public APIs, version management, and thorough testing in ways I hadn’t before. I’m proud of how far the AI Helper Library has come, but I’m also excited to see where it goes next—especially if developers out there contribute ideas or improvements. If you do try it, let me know what you think. Here’s to many more learning moments ahead!