Full-Stack AI Application Developer
Build production web apps powered by LLMs — backend to frontend.
A technical learning path for developers who want to build real AI applications. Covers LLM API integration, embeddings, RAG pipelines, vector databases, and production deployment.
5 Stages
Objective
Call LLM APIs programmatically from code
Key Concepts
- API keys and authentication
- Chat completions API
- Streaming responses
- Token counting
- Rate limiting
Tools You'll Use
Tasks
- Set up API accounts and get keys (use environment variables)
- Build a simple script that calls an LLM and prints the response
- Add streaming so text appears as it generates
- Build a chat interface that remembers conversation history
- Implement error handling and retry logic
Milestone
You can build a chatbot in under 50 lines of code that streams responses
Objective
Create a Retrieval-Augmented Generation pipeline
Key Concepts
- What are embeddings
- Chunking strategies
- Vector similarity search
- RAG architecture
- Context window management
Tools You'll Use
Tasks
- Create embedding chunks from a document (your company docs, a book, etc.)
- Store embeddings in a vector database
- Build a semantic search that finds relevant chunks for any question
- Combine retrieved chunks + user question → LLM prompt → answer
- Test: compare answers WITH and WITHOUT RAG on the same questions
Milestone
Your RAG system answers questions correctly based on your source documents, not just general knowledge
Objective
Design a complete AI application architecture
Key Concepts
- System design
- API routes structure
- Database schema
- Authentication flow
- Caching strategy
- Cost monitoring
Tools You'll Use
Tasks
- Design your app: what problem does it solve for whom?
- Create a data model (what users? what content? what history?)
- Build the API routes: chat, document upload, search, user management
- Set up user authentication
- Add logging and cost tracking per user
Milestone
You have a working app where users can sign up, upload documents, and chat with them via RAG
Objective
Deploy your app so real users can access it
Key Concepts
- Vercel/Cloudflare deployment
- Environment variables
- Domain + HTTPS
- Performance optimization
- Monitoring
Tools You'll Use
Tasks
- Set up CI/CD deployment (push to GitHub → auto-deploy)
- Configure all environment variables securely
- Purchase and connect a custom domain
- Run Lighthouse + fix critical issues
- Add error monitoring (Sentry recommended)
Milestone
Your app is live on a custom domain, serving real users, and you get alerted when errors happen
Objective
Grow and improve your AI product
Key Concepts
- Feedback loops
- A/B testing prompts
- Cost optimization
- Vector DB tuning
- New feature iteration
Tools You'll Use
Tasks
- Talk to 10 real users: what do they love? what frustrates them?
- Identify the #1 improvement to make
- Optimize: what's your cost per user? Can you reduce it?
- Add one new feature based on user feedback
- Plan your quarter 2 roadmap
Milestone
You have 100+ active users, know your retention rate, and have a clear plan for growth