AI Project Starting Point: From AI Quiz to Productization Practice

1. Introduction

Nowadays, the wave of innovation in the AI field is sweeping across the globe. From chatbots to image generators, new projects are launched almost every day. According to the latest market data in 2025, ChatGPT firmly leads with about 60% market share, Microsoft Copilot accounts for 14%, and Google Gemini about 13.5% (Meetanshi, 2025), while Anthropic’s Claude only accounts for around 3.6% (First Page Sage, 2025; TapTwice Digital Team, 2025). ChatGPT not only has a massive user base but also offers free usage options, greatly lowering the entry barrier.

However, despite the surge of AI applications in the market, the vast majority of products struggle to surpass ChatGPT in user experience and functional depth. Many developers are keen to replicate ChatGPT’s features but overlook the product’s core value and user needs, resulting in high-cost investments that fail to yield corresponding returns. The real challenge is not “whether you can call a model API,” but how to integrate AI deeply into product logic and user experience, making AI the driving force for solving real problems.

This article will demonstrate through a concrete example—the AI Quiz project—why it is an ideal starting point for beginners and indie developers to step into AI productization.

2. Why Choose a Quiz as the Starting Point for an AI Project

Beyond technical controllability and scalability, quiz-type needs are also extremely common in enterprises and teams. Many companies like to use quizzes during morning meetings, team building, or group activities to enliven the atmosphere, promote communication, and share knowledge. According to the latest research, more than 80% of enterprises recognize that quiz activities can effectively improve employee engagement and team cohesion (TeamOut, 2025; ProProfs, 2025; Quizado, 2025).

However, many teams find it difficult to find quiz software that fits their interests and professional fields, especially content that aligns with specific topics and cultures. Although there are many quiz products on the market, most have single content and lack personalization, making it hard to meet diverse needs. This simple yet important demand is instead overlooked by most developers, leaving vast space for innovation and service.

The AI Quiz project has multiple advantages, making it an ideal entry point for beginners and indie developers:

• Clear scope, easy to control: The core functional boundaries of AI Quiz are clear, covering question generation, answer verification, and explanation feedback. The project is of moderate size, facilitating rapid iteration and debugging, and is an ideal testing ground for verifying AI logic.

• Direct and significant user value: Whether for learning, entertainment, or knowledge reinforcement, quizzes have clear use scenarios. Users receive immediate feedback, and developers can directly observe the manifestation of product value, enhancing practicality and appeal.

• Strong scalability with room to grow: AI Quiz can naturally extend to advanced features such as personalized recommendations, adaptive difficulty, mistake analysis, and learning path planning, laying a solid foundation for subsequent product upgrades.

• Mature and low-cost tech stack: With the combination of Next.js, Supabase, and Vercel, you can quickly build and launch a prototype, achieving integrated front- and back-end development while balancing efficiency and economy. Today, many platforms such as OpenRouter and Google Gemini provide free AI API calls covering various advanced models, greatly lowering the development threshold (CometAPI, 2025; Aimlapi, 2025). For relatively simple tasks like generating quizzes, the required model capabilities are not complex. Developers do not need to rely on high-cost or complex models to achieve high-quality question generation and answer explanations. This makes the AI Quiz project not only technically easy to implement but also cost-effective, suitable for beginners and indie developers to quickly validate and iterate.

3. AI Quiz Project in Practice

NZLouis AI Quiz is developed based on Next.js and the Vercel platform (live project: https://quiz.nzlouis.com), with integrated back-end and front-end to support rapid iteration and deployment. The project calls multiple free AI APIs provided by OpenRouter as well as the Google Gemini API, covering many advanced and free models, including GPT-OSS 20B, DeepSeek V3.1, Llama 3 70B Instruct, Qwen, MAI-DS-R1, Kimi K2 Instruct, and Nous DeepHermes 3 Llama 3 8B Preview, meeting the needs of generating quizzes of different types and difficulty.

Users can freely enter any topic of interest, or generate related topics through AI recommendations, enhancing content diversity and personalization. The system supports selecting the difficulty level and the number of questions in the quiz, with common options including 5, 10, 15, and 20 questions. Users only need to click the generate button to enter the answering mode and experience the intelligent quiz process.

During the answering process, if users encounter difficult problems or have no ideas, they can choose the “Hint” button, and the system will automatically generate hints for the relevant questions to help users clarify their thinking rather than directly giving the answer. At the same time, clicking the “Ask AI” button will pop up an AI assistant panel on the right. The AI will provide suggestions to assist thinking for the current question, guiding users to analyze and reason, promoting independent thinking and learning rather than simply telling the correct answer.

This project fully leverages the advantages of multiple models, combined with flexible parameter configurations, to achieve an efficient and user-friendly quiz generation and answering experience, suitable for beginners and indie developers to quickly validate AI productization ideas.

Project source code: https://github.com/NZLouislu/nzlouis-ai-blog

The introduction of AI has brought intelligent potential to traditional quiz systems:

• Intelligent question generation: Large models can automatically generate diverse, difficulty-adaptive questions, avoiding templated content.

• Intelligent explanations and feedback: Not only telling users “right” or “wrong,” but providing detailed logical explanations and study tips to enhance educational effectiveness.

• Personalized difficulty adjustment: AI dynamically adjusts the difficulty of the question bank according to users’ answering performance to achieve adaptive learning.

• Data-driven optimization: Combined with user behavior records in the database, AI can analyze learning curves and continuously optimize content recommendations (features still under development...).

4. User Experience Is the Key to Project Success

In terms of user interface design, the AI Quiz project adopts a comfortable and friendly visual style. The page background uses a soft sky-blue tone, paired with warm sun elements, creating a bright and relaxed atmosphere. The floating white cloud animation in the background further enhances the pleasant user experience, helping users maintain a good mood and focus during the quiz.

In addition, the interface design fully considers multi-device adaptation, supporting various screen sizes and view modes. Whether on desktop, tablet, or mobile, it ensures reasonable layout of interface elements and smooth interaction, improving overall usability and user satisfaction.

5. AI Invocation Strategies and Best Practices

In actual development, AI is not omnipotent, nor is it a free lunch. The following are key strategies for building an efficient and controllable AI Quiz:

• Core logic under your control: Keep the quiz structure, scoring, and user logic implemented locally, letting AI participate only in content generation to prevent model runaway or irreproducible results.

• Control invocation costs: Combine open-source LLMs, local inference models, and low-cost APIs to balance cost and performance.

• Precise prompt design: High-quality prompts are key to generating stable results. Template-style prompts should be designed for different question types to ensure output consistency.

• Caching and reuse mechanisms: Common questions and explanation results can be cached in the database to reduce repeated calls and improve response speed.

6. From Quiz to Broader AI Applications

A successful AI Quiz is not just a practice project, but a bridge to larger AI applications.

• Enrich user experience: Introduce features such as leaderboards, mistake notebooks, and learning path recommendations to enhance interactivity and immersion.

• Expand across domains: Extend from quizzes to interview question training, language learning, and professional assessments, enabling AI to truly serve different groups.

• Upgrade product thinking: AI is not just a functional module; it should become part of user experience design—only when AI is deeply integrated with scenarios does the product truly “come alive.”

7. Introduction to Vibe Coding

The development process of this AI Quiz project is itself a practice of the Vibe Coding concept. Nowadays, industry research (such as a study by Google) and predictions are increasingly common, suggesting that in most tech jobs, up to 90% of daily tasks have already been assisted by AI (CNN, 2025). This new programming paradigm, with developers as the lead and AI as an efficient execution partner, is called Vibe Coding. It is dedicated to helping developers efficiently call advanced AI models, supporting multiple API access methods to meet different development needs and budgets.

First, Vibe Coding supports calling the OpenAI API and Claude API. These services are billed by token, with higher costs, but can provide powerful model capabilities, suitable for projects with higher performance requirements. The Google Gemini API is also billed by token but has a certain amount of free usage per day, suitable for small to medium-sized projects. In the AI Quiz project, I used the Gemini API and found that its stability and response speed both performed excellently.

For developers with limited budgets, OpenRouter provides some free but limited-capability AI model APIs, which can be invoked through VS Code plugins such as Kilo Code, suitable for entry-level and lightweight applications. Of course, state-of-the-art models can also be called and billed by token.

In addition, Vibe Coding supports integration with VS Code and other IDEs, used in conjunction with tools such as Windsurf, Cursor, GitHub Copilot, and CodeBuddy. These tools typically require payment, but the advantage is the ability to call the most advanced models, such as GPT-5 and Claude 4.5, which greatly improves development efficiency and code quality. It is recommended that developers try these IDEs or plugins first and purchase after confirming suitability.

Through diversified invocation strategies and tool support, Vibe Coding helps developers find the best balance between cost and performance, accelerating the implementation and innovation of AI projects.

This article mainly focuses on the introduction of the AI Quiz project. I will write a separate blog to explain in detail the selection and usage of Vibe Coding and related tools and share my hands-on experience.

8. Summary and Insights

The best starting point for an AI project is a “small, complete, and shippable” practice project. The true value of AI lies in improving experience and efficiency rather than showing off.

Starting from a simple AI Quiz, you can not only master engineering methods of AI applications but also gradually move toward product thinking and commercial exploration.

The core philosophy of Vibe Coding: understand AI through hands-on building and realize innovation through productization. Start from the quiz and let your first AI project truly “have a soul.”

For a deep dive into implementation details, see the GitHub repository: https://github.com/NZLouislu/nzlouis-ai-blog

9. References

1. First Page Sage. (2025). Top generative AI chatbots by market share – October 2025. Retrieved from https://firstpagesage.com/reports/top-generative-ai-chatbots

2. Meetanshi. (2025). Google Gemini (formerly Bard) statistics 2025. Retrieved from https://meetanshi.com/blog/google-gemini-statistics

3. TapTwice Digital Team. (2025). 7 Anthropic statistics (2025): Revenue, valuation, users, funding. Retrieved from https://taptwicedigital.com/stats/anthropic

4. TeamOut. (2025). 30 employee engagement statistics that you need to know in 2025. Retrieved from https://www.teamout.com/blog-post/30-employee-engagement-statistics

5. ProProfs. (2025). 50+ best team quiz ideas to energize virtual team meet-ups. Retrieved from https://www.proprofs.com/quiz-school/blog/best-team-quiz-ideas

6. CometAPI. (2025). Which generative AI APIs are free? - CometAPI - all AI models in one API. Retrieved from https://www.cometapi.com/which-generative-ai-apis-are-free

7. Aimlapi. (2025). Best AI API's 2025 for free. Retrieved from https://aimlapi.com/best-ai-apis-for-free

8. Quizado. (2025). 7 benefits of quiz games online for team building. Retrieved from https://quizado.com/blog/7-benefits-of-quiz-games-online-for-team-building

9. CNN. (2025, September 23). Google study suggests 90% of tasks in most tech jobs could be impacted by AI. Retrieved from https://edition.cnn.com/2025/09/23/tech/google-study-90-percent-tech-jobs-ai