Learn the right AI path for your role.
We focus primarily on Applied AI for developers: how to integrate modern AI into web and mobile applications, how it differs from Gen AI product work, and where AI/ML engineering becomes a separate path.
Applied AI vs Gen AI vs AI/ML Engineer
These are related, but they are not the same career path. We make the distinction explicit so students do not prepare for the wrong role.
Applied AI
Best for full stack, backend, mobile, and product-oriented developer roles.
Applied AI is about integrating existing AI capabilities into products. You learn how to use models, prompts, APIs, workflows, and evaluation inside real apps.
Syllabus Focus
- AI fundamentals for developers
- Prompt design for reliable outputs
- OpenAI API usage and response handling
- Chat, summarization, and classification features
- RAG basics, retrieval patterns, and grounding
- Guardrails, moderation, and output validation
- Cost, latency, and quality tradeoffs
- Deployment patterns for production apps
Gen AI
Best for Gen AI app builders, AI product developers, and LLM-focused application roles.
Gen AI focuses on building user-facing experiences with language and multimodal models. It is closer to LLM product engineering than classical ML.
Syllabus Focus
- LLM concepts and model capabilities
- Prompt chaining and structured outputs
- Embeddings, semantic search, and vector basics
- Context management and conversation memory
- Tool calling and agentic workflows
- Evaluation of LLM outputs and failure cases
- Gen AI UX patterns for product teams
- Shipping Gen AI features safely
AI/ML Engineer
Best for ML engineering, data-heavy AI roles, research-support roles, and model training pipelines.
AI/ML engineering is about datasets, model training, experimentation, performance, and serving. It is deeper on math, data, and model lifecycle than our AI program.
Syllabus Focus
- Python for ML workflows
- Statistics and model evaluation basics
- Data preprocessing and feature engineering
- Supervised and unsupervised ML concepts
- Training pipelines and experimentation
- Model deployment and serving basics
- Monitoring drift and model quality
- MLOps awareness
Our focus: Applied AI for developers
This program is built for students who want to ship AI features inside full stack web and mobile products, not for students looking for a model-training-heavy ML curriculum.
Real integration skills
You learn to ship AI inside existing web and mobile stacks, not as isolated demos.
Backend-first execution
We teach where AI actually lives in production: APIs, workflows, guardrails, storage, and observability.
Product thinking
You learn which AI feature fits which product problem instead of adding AI as a gimmick.
Interview-ready projects
The goal is demonstrable work you can explain clearly to hiring teams.
How AI plugs into web and mobile
We teach AI as part of the product architecture: UI, backend, data flow, reliability, and deployment.
Web Full Stack Integration
- React chat interfaces with streaming responses
- Node.js, Spring Boot, and Python backend API integration
- Authentication-aware AI endpoints for user-specific context
- RAG-driven search, document Q&A, and internal tools
- Admin dashboards for prompts, logs, and usage analytics
- Async jobs for long-running AI workflows
Mobile Integration
- Android, iOS, and Flutter AI-assisted features
- Voice, text, and assistant-style mobile flows
- Backend-mediated model access for security and control
- On-screen summarization, recommendation, and support features
- Offline-aware UX and fallback handling
- Production patterns for token usage, retries, and error states
Full Stack Delivery
- Designing the frontend, backend, and data flow together
- Model selection based on cost, speed, and output quality
- Logging, evaluation, and prompt iteration loops
- Rate limiting, caching, and safety checks
- Deployment and environment management
- Portfolio-ready AI features you can demo in interviews
Built for developers who want to stay current.
If your goal is to become a stronger full stack or mobile developer with modern AI integration skills, this is the correct path. If your goal is deep model training or ML research workflows, that is a different specialization.
See AI integrated into real apps
Book a free demo and understand which AI path fits your career, and how we teach full stack web and mobile integration end-to-end.