Full-Stack Engineering with Next.js & AI Features
Ship a production full-stack app on Next.js, TypeScript, and modern edge infrastructure — with LLM features integrated the way real product teams do it.
Duration
10 weeks · 5-7 hours/week
Level
Intermediate
Delivery
Hybrid
Status
Open for enrollment
Why this technology matters.
Full-stack engineering with Next.js is how modern product teams ship complete web applications — typed frontend and backend, auth, payments, and observability on edge infrastructure. It matters now because users expect fast, reliable apps and teams expect one codebase that carries an idea from prototype to production.
It is used by product teams and founders to build SaaS products, marketplaces, and content platforms with server rendering, API routes, and LLM features integrated production-grade. It solves shipping whole products quickly with type safety and testing, but it does not solve finding product-market fit or defining good AI behavior — a polished app around a confused workflow still confuses users, and RAG cannot fix missing source content.
By the end you will be able to build a typed, tested full-stack app with auth, payments, and observability, an LLM feature integration with RAG and structured outputs, and a deployed production release on modern edge infrastructure.
Why this course exists
The gap is between a tutorial app on localhost and a production product that handles real users, real money, and real AI latency and cost. The course teaches the arc from Idea to Design to Code to Test to Deploy to Operate, with Model-to-Production concerns woven into the AI features, so students ship like product teams do.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- Comfortable with JavaScript and React basics
- Familiarity with APIs and databases
- Experience with git and Node tooling
Technologies & tools
Skills you'll gain
A 10 weeks arc, module by module.
- Module 01
Weeks 1–2 — Next.js, TypeScript, and data layer
- Module 02
Weeks 3–4 — Auth, payments, and access control
- Module 03
Weeks 5–6 — Background jobs, queues, and observability
- Module 04
Weeks 7–8 — LLM features: RAG and structured outputs
- Module 05
Weeks 9–10 — Capstone: ship to production
Practical Training Flow
Learning → Guided Labs → Independent Practice → Industry Project → Capstone → Portfolio → Career Preparation. Practical hours are tracked alongside instructional hours and surfaced on the certificate.
Delivery as HIGAET Practical Training / Experiential Learning.
What you'll be able to do.
- Build a typed, tested full-stack app with auth, payments, and observability.
- Integrate LLM features (RAG, structured outputs, agents) production-grade.
- Operate on modern edge/serverless platforms with CI and rollbacks.
- Ship a portfolio app ready for hiring conversations.
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
Typed Next.js app with auth and payments
- Project 02
RAG feature with structured outputs
- Project 03
Agent integration with production guardrails
- Capstone
Production full-stack app with LLM features and observability
Speak the language first.
- Next.js app router
- Organizes pages, layouts, and routes with server and client components for a typed full-stack app.
- TypeScript end-to-end typing
- Shares types between frontend, API routes, and database models to catch errors before runtime.
- Authentication and sessions
- Verifies user identity and manages sessions so protected pages and APIs stay secure.
- API routes and server actions
- Handles backend logic inside the Next.js app for forms, payments, and data mutations.
- Retrieval-augmented generation (RAG)
- Grounds LLM answers in retrieved documents so responses are more accurate and traceable.
- Structured outputs
- Constrains model responses to a defined schema so the app can validate and render them reliably.
- AI agents in products
- Lets a model call app tools step by step to complete tasks, with limits and review for safety.
- Edge deployment
- Runs the app close to users on edge infrastructure for lower latency and simpler scaling.
- Observability
- Uses logs, metrics, and traces to monitor app health, payments, and LLM feature quality.
Fix, check, and go deeper.
Troubleshooting & common mistakes
Build fails on server versus client component boundary
Check for client-only hooks in server components, move interactivity into marked client components, and rebuild.
Auth session missing on protected API route
Verify session cookies and middleware config, confirm the route checks the session, and test with a fresh login.
RAG answers cite irrelevant documents
Inspect chunk size and retrieval ranking, tighten filters, and re-test with a small set of known questions.
LLM structured output fails schema validation
Constrain the response schema, retry with validation and a repair pass, and log failures for review.
Payment webhook not updating order status
Verify webhook signatures and idempotency handling, replay a test event, and check logs for rejected requests.
Edge deployment shows stale data after updates
Review caching and revalidation settings for the affected route, then adjust revalidation and redeploy.
Before you move on, you should be able to
- Explain Next.js routing with server and client components
- Build a typed full-stack app with auth and payments
- Design API routes and server actions for app workflows
- Build production-grade RAG with structured outputs
- Evaluate LLM feature quality with test queries
- Deploy a Next.js app to edge infrastructure with observability
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
Common questions
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A 10 weeks course — Bootcamps.