Hatch Coding needed to improve its 'learn-to-code' product with higher user engagement, enhanced learning outcomes, and stronger customer retention.
Rootstrap improved not only the conversion funnel and product onboarding with better UX/UI design and tech stack enhancements, we also built custom AI-powered product features that drove significant increases in user engagement and MRR.
The Challenge
Rootstrap faced several hurdles in enhancing Hatch Coding’s platform:
- Low Conversion: The conversion funnel and onboarding process was below industry standard.
- Low Engagement: The product wasn't sticky enough with young coders to drive high repeat engagement.
- Technical Debt: The platform was built on outdated code with minimal documentation and multiple security vulnerabilities.
- Poor Scalability: Many different frontend and backend technologies hindered the platform's ability to scale efficiently.
Our Solution
Rootstrap drove stronger growth metrics by modernizing Hatch Coding's conversion funnel and onboarding flow, and also drove stronger retention metrics with multiple new AI-powered product features.
In Focus: AI-Powered Product Features
- "Solve with AI" Feature: Students can use this tool to prompt AI for help on a specific coding project and receive personalized guidance in real-time. By providing instant, conversational feedback, young learners were more engaged and accelerated their problem-solving skills.
- "Explain with AI" Feature: Students can use this tool to prompt AI with a code segment and receive detailed explanation for how it works, making complex concepts easier to understand. This personalized, self-directed approach improved engagement and learning outcomes as well.
- Prompt Engineering & Fine-Tuning: We modified a foundational model to use Hatch Coding source documents and data, and iterated based on feedback gathered from internal testing and user interactions.
- Rapid Development and Deployment: Our team built and tested these features fast, bringing these to market for Hatch Coding from beginning to end in less than 3 months.
Tech Stack Enhancement
- Backend: Upgraded to Python 3.7 with the Django 2.2 framework for improved performance and scalability.
- Frontend: Integrated Vue.js (v2) with Django Templates to create a dynamic, user-friendly interface.
- AI Solution: Utilized OpenAI APIs and Python libraries to power the AI features efficiently.
- Cloud Deployment: Deployed on Amazon Web Services (AWS) infrastructure, utilizing EC2 instances for scalable and reliable hosting.
Prompt Engineering Techniques
We applied best-in-class prompt engineering techniques to optimize AI performance:
- Chain-of-Thought Reasoning: Leveraged this emerging 'mega-model' approach—the foundation for OpenAI's latest models—to construct LLM applications that think through problems step-by-step, enhancing the quality of assistance provided to students.
- Few-Shot Learning: Included specific examples in prompts to align AI outputs with Hatch Coding's standards, improving the relevance and accuracy of responses.
- Role Specification: Instructed the AI to adopt roles like "expert programmer" to provide contextually relevant assistance, making interactions more meaningful for learners.
- Iterative Development: Started with simple prompts, refining them through multiple iterations to achieve precise outputs that meet educational objectives.
- Error Handling: Designed prompts to guide users back on track with clear error messages when non-code inputs were detected, enhancing the user experience.
- Model Selection: Chose appropriate AI models for each task, balancing performance and cost to deliver complex coding assistance and explanations effectively.
- JSON Output Formatting: Enhanced response structure and quality using OpenAI's JSON mode, ensuring consistent and reliable AI outputs.
Safety and Compliance
- Content Safety: Integrated OpenAI's Moderation API to filter inappropriate content, ensuring a safe learning environment for young users.
- Prompt Injection Prevention: Implemented safeguards against misuse of AI prompts to maintain the integrity of the platform.
What's On Our Roadmap
While we gather initial performance data to determine final next steps, Rootstrap and Hatch Coding are prepared to explore the following enhancements:
- AI Model Fine-Tuning: Customizing the AI model with more proprietary data and reference examples to further improve accuracy and relevance.
- Personalized Learning Paths: Developing AI-driven adaptive learning experiences tailored to individual student needs, fostering deeper engagement and better outcomes.
- Gamification Elements: Introducing interactive challenges and rewards to make learning more exciting and to keep pace with evolving educational trends.
Business Impact
The collaboration resulted in significant benefits for Hatch Coding:
- Increased User Engagement: The AI-powered features made coding lessons more interactive and engaging, boosting user retention and time spent on the platform.
- Higher Conversion Rates: Improved onboarding and user experience led to an increase in paid subscriptions, MRR, and LTV.
- Enhanced Learning Outcomes: Students demonstrated better understanding and interest in coding, validating the effectiveness of the AI integration and leading to positive word-of-mouth referrals.
- Strengthened Safety Measures: Robust safety protocols enhanced trust among parents, educators, and stakeholders, positioning Hatch Coding as a reliable educational resource.
What Our Client Says
“Rootstrap is very responsive, communicative, and paid attention to budget and timeline. All was good with this project.”
— Peter Kuperman, Hatch Coding CEO & Founder
Partner with Rootstrap
This case study exemplifies how Rootstrap's expertise in AI, data engineering, and software development can drive improved product performance and increased business outcomes.
Drive efficiency, accelerate your development, and innovate with a data-driven, results-oriented team that knows how to build great software. Rootstrap offers exceptional talent with an owner’s mindset to make it happen.
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