This talk covered a practical GitHub template I built for quick GenAI project prototyping. It’s based on Nx monorepo and packs NextJS for frontend, NestJS for backend, and FastAPI for AI services. For low-code work, I’ve included Langflow and n8n, with PostgreSQL and MongoDB handling data storage needs. There’s also Ollama integration for anyone wanting to run local LLMs. Everything deploys via Docker Compose configs in the .containers folder. I’ll also go through the VS Code extensions that make development faster. This setup eliminates hours of boilerplate work, letting you jump straight into building your GenAI projects.
A useful decision for a GenAI prototype
Before choosing components from the public prototype repository, define one question the prototype should answer. For example: can a user complete a specific task with representative inputs, and can someone review the output?
A reusable project structure reduces setup work. It does not establish that a proposed product is useful or ready to operate. Decide what evidence would justify continuing before adding more services.
If the idea and test are defined, see the implementation sprint. If you still need to identify a worthwhile business problem, see the company audit.