This weekend, I built and shipped a prototype AI feature inside our Chain.io platform. What began as a personal curiosity about prompt engineering turned into a practical experiment that resulted in production-ready code, submitted for internal review—all while only hand writing a few lines of code.
Step 1: Ask the Right Prompt
I started with this prompt to ChatGPT:
Build me a personalized prompt engineering curriculum. I am a deeply technical business leader, so I'll have a wide range of business use cases from product development to sales support. I'll work across multiple models, but if it's better to learn one first, the GPT-4 is preferred. I want to spend a few hours per week. I learn very fast, so you can be aggressive with the curriculum. I need to build AI into every facet of our company which will involve bringing in lots of data and integrations and using agentic strategies.
ChatGPT responded immediately with a structured curriculum. The first lesson was a foundational YouTube video: Prompt Engineering 101. It was solid, though a few years old, so I asked ChatGPT to summarize what had changed since the video was published. The context was clear and helpful.
Step 2: Apply It Immediately
During the hands-on exercise, I started thinking about real-world applications. Specifically, I wondered if this approach could help summarize the long and often complex integration logs we deal with at Chain.io. I signed up for an OpenAI account, added $50 in credits, and got started.
Step 3: Prototype with Tools I’d Never Used
Using Windsurf, I connected our GraphQL API to OpenAI’s API and tested the setup in a Jupyter Notebook—something I hadn’t worked with before. Within about an hour, I had a functional prototype that could summarize our actual log data using AI.
Step 4: Track Costs in Real Time
To ensure the feature would be cost-efficient in practice, I built a spreadsheet to track per-call API costs. Windsurf helped me iterate on prompt design so I could reduce token usage and target only the necessary data. This helped strike a balance between effectiveness and expense—critical for both scalability and budget discipline.
Step 5: Backend Integration with AI as My Copilot
Once the prototype met my expectations, I integrated it into our backend. Using Windsurf, I embedded the AI logic directly into our existing API. The core logic came together quickly, and I had time to implement structured logging, usage controls based on license tiers, and other necessary guardrails—details that often slow projects down when done manually.
Step 6: Build the Frontend Without Touching Code
To wrap things up, I used Windsurf again to build a React component to present the AI-generated summaries. I offered some UI feedback and adjusted design elements, but didn’t have to dive into the code myself.
From Idea to Feature in One Day
Between morning coffee and bedtime—and with time carved out for lunch, family dinner, and a neighborhood walk—I had a complete feature prototyped, implemented, and ready for review.
YMMV
As someone with decades of experience in both coding and business, I had an advantage going into this mini-project. Your mileage may vary.
But the key takeaway here isn’t about technical skill—it’s about approach. This kind of work is much more accessible than it might seem from the outside.
My most important takeaways:
Pick focused problems: I didn’t try to reimagine our entire product. I just added a single, useful feature.
Do something: A lot of this sounds complex in theory, but becomes manageable when you actually try it. Even if your first attempt doesn’t work, you’ll come away smarter.
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