Discover how to take your app idea from concept to high-fidelity MVP with lightning speed in this hands-on demo! You’ll learn how to organize product requirements, train AI tools using your own user stories, and craft powerful prompts that supercharge no-code and low-code platforms like Lovable and Thunkable. Watch step-by-step as we merge user insights, automate prototype creation, and iterate rapidly to build a functional, customizable app without writing code. Whether you're a founder, designer, or developer, this demo will empower you to launch better products, faster.
After watching this video, viewers will be able to efficiently structure and document their product ideas, train AI tools with custom user stories and requirements, and generate detailed prompts for building full-featured app prototypes. They'll learn how to merge, organize, and optimize user stories to maximize productivity and reduce costs with AI-driven app builders like Lovable and Thunkable. By following these steps, viewers can rapidly create, customize, and iterate on high-fidelity MVPs, preparing their apps for further refinement and deployment. This workflow empowers users to leverage multiple no-code platforms and streamline their app development from concept to actionable prototype.
Following are the key things you will be able to do after you watch this demo:
Understanding Pricing and Pre-Composing Chats 0:11
Josh Lomelino explains the importance of understanding pricing in AI apps, emphasizing that credits are tied to prompts and chats.
He advises pre-composing chats in tools like ChatGPT to avoid high costs in apps like Lovable, which charge based on daily credits.
Josh demonstrates how to go back to prior steps in ChatGPT to train the system on user stories and features.
He highlights the need to ensure the chat is trained universally across all chats, otherwise, it needs to be asked to do so explicitly.
Training and Managing Chats 4:53
Josh discusses the process of training chats on system functionality, using SRT files as an example.
He explains the incremental compounding of work in Lovable, which makes it costly to start chatting without a well-defined prompt.
Josh emphasizes the importance of optimizing the use of credits to avoid high costs, comparing it to the cost of a development team.
He mentions the potential for the browser to choke on large chats and the need to break them into manageable parts.
Merging and Organizing User Stories 7:17
Josh demonstrates how to merge multiple chats to create a faster and more efficient chat.
He explains the process of outputting user stories as a CSV and the challenges with special characters in CSV files.
Josh suggests exporting as an Excel file to fix formatting issues.
He highlights the importance of incrementally building a pipeline to automate the creation of front-end interface screens.
Enhancing User Stories with Features and Acceptance Criteria 9:36
Josh adds a feature column to the user story backlog, differentiating it from user story language.
He includes acceptance criteria, which helps in testing and identifying the area within the app where the feature would exist.
Josh emphasizes the importance of documenting key wins and moments in a Google Doc for future reference.
He explains the process of comparing the current chat output with a saved Word file to ensure completeness.
Creating a Master Prompt for Lovable 17:44
Josh discusses the process of creating a master prompt for Lovable, which includes context, logical structure, explicit instructions, and adaptive considerations.
He highlights the need for granular detail to get specific UI controls in the prompt.
Josh explains the importance of saving the output as a Google Doc or GitHub repository for version control.
He demonstrates how to rewrite the master prompt to include all features in one MVP release.
Training Lovable on Documentation 42:48
Josh trains Lovable on the documentation of the tool, which helps in creating a prompt for Lovable.
He explains the process of crawling through the documentation pages and listing the pages learned from.
Josh emphasizes the importance of checking that the AI is actually doing what it claims to do.
He demonstrates how to extract and summarize recommendations from the AI.
Refining and Customizing the App 45:00
Josh refines and customizes the app by adjusting colors and mastering prompting.
He explains the process of using chat mode to plan additional features like a coach and admin portal.
Josh demonstrates how to toggle between different device types to test the app on various form factors.
He highlights the importance of iterating on the app to ensure it meets user needs and pain points.
Exploring Different Tools and Integrations 49:51
Josh explores different tools like Thunkable, Bubble IO, Cursor, Replit, Flutter Flow, and Draftbit.
He explains the process of training the AI on the documentation of these tools to create a single prompt.
Josh highlights the importance of integrating tools like Supabase and Airtable for data management.
He emphasizes the need to experiment with different tools to find the best fit for the project.
Finalizing the MVP and Next Steps 1:04:33
Josh finalizes the MVP by ensuring all features are included in the prompt.
He explains the process of exporting the code base and pushing it to GitHub for further development.
Josh highlights the importance of iterating on the app to ensure it meets user needs and pain points.
He explains the next steps of refining and customizing the app, and preparing it for deployment to the app stores.