Why Even Create Prompt Space ?
The emergence of Generative AI marks a remarkable milestone in human achievement. However, its potential may seem wasted if one doesn't know how to utilize the technology effectively to enhance productivity. A friend of mine recently experienced this dilemma when he purchased credits on a platform, hoping to use AI prompts to generate high-quality content efficiently. He needed the AI to craft a write-up that didn't obviously appear to be machine-generated but rather reflected his own voice. Despite his efforts, he found himself burning through his limited credits without much success.
Observing his struggle, I offered to assist by refining the prompt. With a better-crafted input, the machine executed the task flawlessly. This experience sparked a realization: How many others might be facing similar challenges? What if we could share our successful prompts? It occurred to me that many prompts we struggle to formulate have likely been perfected by others. The solution was to create a platform where users can share effective AI prompts, enabling individuals to leverage collective expertise and optimize their AI interactions**. Prompted Space** was born.
The Choice of Tech Stack
As with any project destined for success and longevity, it all boils down to design and planning. And that planning begins with the crucial step of selecting the right technology stack to build your project.
One of the most significant non-technical challenges faced was deciding on the appropriate tech stack for the project. Initially, I considered using FastAPI with Python or Flask, as these were familiar frameworks from my previous experience and aligned with the roadmap set by the project requirements. However, given the need for rapid development and integration of OAuth authentication, I had to reconsider my approach. Ultimately, I opted for Next.js, the official React framework, which offered a more expedient path to meeting the MVP deadline.
When it came to selecting a database, my initial inclination was towards SQL due to my familiarity with it from my studies with ALX. However, considering the constraints of budget and resources available to me, I had to reassess my options. I realized that my application didn't require intricate relational structures. If simplicity in querying and handling large volumes of data was the priority, then NoSQL seemed like the ideal solution. After weighing the options, MongoDB emerged as the best fit for my needs. While I did consider Firebase, its serverless nature imposed constraints on development that didn't align with our requirements, prompting me to opt for MongoDB. Additionally, cost considerations also favored MongoDB over Firebase, which appeared to be comparatively expensive.
Non-Technical Challenge
Another challenge was developing a viable business plan for the project. Monetizing AI products, especially in the era of generative AI, presents unique challenges. Crafting a sustainable revenue model required careful consideration of various factors, including market analysis, revenue streams, and scalability. Additionally, creating a project roadmap and business model canvas posed its own set of challenges. I needed to prepare a compelling pitch deck for potential investors, who typically prefer to see a functional MVP before committing funds to an idea.
Current Feature and Plans
Successfully built the Landing page for the project.
Implemented functionality for users to upload their prompts.
Integrated Google OAuth for user authentication.
Implemented features for users to update and delete their prompts.
Allow other user to copy user prompt
Build the search bar to help user search other AI prompt
Future Plan
Plan to support local authentication using JWT tokens for enhanced security.
Integrate Momo Money API into the project to facilitate advanced prompt sales.
I am happy to say there is a working MVP of this project deploy on varcel
In conclusion, I've come to understand that there's no perfect solution when designing a system. It all depends on the project and budget. That's why it's often beneficial to conduct extensive research. Also, patience is key during the process because sometimes you might over-engineer what could have been achieved with a simple design. Understanding your users' needs and meeting them is crucial. Moreover, cultivating grit is essential; you don't need to comprehend every aspect of a technology before using it to solve problems. Embrace learning on the go.
I'm George S Mulbah II, a self-taught software engineer and mathematician with 8 years of experience. Additionally, I hold a Diploma in Electronics Engineering. Currently, I'm a student at ALX, where I'm undergoing professional training. Apart from my technical pursuits, I'm passionate about entrepreneurship, reading, and meditation. I also identify as a Christian and a follower of Christ.
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