Question: How do you integrate AI with React/Next.js development?

Answer: Working on a recent project in Pubgenius has been a deep dive into full-stack AI integration. On the frontend, I use React to stream LLM responses dynamically. Each chat interaction renders live feedback using markdown animations and loading states — all driven by AI responses coming in from a FastAPI backend.

What’s been really cool is how AI (via tools like ChatGPT or Claude on Cursor) helps scaffold these experiences. Whether I’m designing component patterns for dynamic AI chat flows, understanding complex concepts and docs on cloud-formation, or wiring up real-time search results, AI speeds up the repetitive tasks so I can focus on the UX and architecture.

On the backend, we orchestrate RAG pipelines using Bedrock, and infrastructure is deployed with AWS CDK — from S3 ingestion to embedding models and vector indexing. I didn’t come in as an infra expert, but AI has helped me break through a lot of that complexity and build confidently across the stack.

Question: How does AI help with Node.js, NestJS, or Express work?

Answer: AI’s been a great accelerant when working with Node.js frameworks like NestJS or Express. I often use AI to scaffold out entire modules — routes, DTOs, guards, and middleware — especially when time’s tight.

What I love most is how it helps translate architectural patterns from other ecosystems (like FastAPI or Django) into Node terms. If I hit an unfamiliar part of NestJS, I can prompt an AI assistant to generate examples or even walk through DI patterns, error handling, or test cases.

It’s also a great second brain for debugging async behaviour, request validation, or edge cases in middleware — stuff that otherwise eats up hours.

Question: How do you use AI to accelerate API development and documentation?

Answer: For API development, I’ve found AI really effective for generating OpenAPI/Swagger docs on the fly. Once I define the core request/response structure, AI helps fill in the rest — paths, descriptions, types — sometimes even test scripts for tools like Postman, Insomnia or HTTPYac.

I also use AI to convert informal business logic or stakeholder notes into API contract drafts. It’s helped shorten the cycle between idea and implementation. Instead of spending hours drafting API docs manually, I can just refine what AI generates and focus on testing and validation.

Question: How does AI help manage database design and migrations?

Answer: AI’s been surprisingly helpful here. When I’m mapping out a new schema or planning a migration, I’ll describe the domain in natural language, and AI will suggest table structures, relationships, and even index strategies.

It’s especially useful when using ORMs like Prisma or TypeORM. I can write a rough schema idea, and AI will convert that to full model definitions — with sensible constraints and field types — and even generate migration scripts.

For legacy or messy data, AI can also assist in writing transformation scripts or suggesting normalization strategies. It gives me a head start and often catches things I’d overlook if I were designing alone.

AI won’t replace engineers. But it absolutely levels us up, especially when you're building across the stack.