AI Training Academies • Full-Stack + AI Developer

Full-Stack Development Plus AI Features

A 30-day program that combines our proven 20-day Full-Stack Developer Academy with two AI weeks that layer real AI features into the application: an LLM-powered feature with streaming and cost controls, a RAG-backed search experience, an eval harness, and AI-assisted CI.

Training Days

Weeks

6–12

Weeks Flexible

Schedule options: Full-time (6 weeks, Mon–Fri) · Half-days (~12 weeks) · Custom cadence for global teams

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AI Tools

Claude API

GitHub Copilot

IDEs & Editors

VS Code

Cursor

Windsurf

Visual Studio

JetBrains

Zed

Backend (Customer-Pickable)

Java / Spring

Python / FastAPI

Node / TS

C# / ASP.NET

Rust

Frontend (Customer-Pickable)

React

Angular

Vue

Svelte

Infrastructure

pgvector

GitHub Actions

AWS

Azure

GCP

Learn Full-Stack Development and Immediately Apply AI Engineering on Top of It

This is the natural evolution of our 20-day Full-Stack Developer Academy. The first four weeks deliver a rigorous full-stack program on the customer's chosen backend and frontend. Weeks 5 and 6 take the application built in weeks 1–4 and systematically add AI features—an LLM-powered capability with streaming and cost controls in week 5, then a RAG-backed search experience, an eval harness, and AI-assisted CI in week 6. Two dedicated AI weeks give the RAG and evals work the time it actually needs instead of cramming it alongside basic LLM integration. Participants leave with both full-stack credentials and the ability to add production-quality AI features to real applications.

Full Mix-and-Match Stack
Java/Spring, Python/FastAPI, Node/TS, C#/ASP.NET, or Rust/Actix × React, Angular, Vue, or Svelte
AI Features on Real Code
Weeks 5–6 build on the app from weeks 1–4
Production Quality Throughout
Tests, CI/CD, cloud deployment included
Flexible Scheduling
Full days, half-days, or custom cadence
Online or On-Site
Delivered wherever your team works
Session Recordings
Review material at your own pace (online delivery)

Week 1: Language Foundations

Backend language fundamentals: syntax, OOP/functional patterns, testing basics

Week 2: Backend API and Database

RESTful API with chosen backend framework + PostgreSQL; auth, testing, CI

Week 3: Frontend

HTML/CSS/JS foundations + chosen frontend framework; consumes the week 2 API

Week 4: Full-Stack Capstone

End-to-end full-stack capstone: containerized, deployed to cloud, CI/CD pipeline, code review

Week 5: LLM Feature

LLM API integration, streaming UX, prompt design, structured outputs, cost tracking, and token budget controls — added to the week 4 application

Week 6: RAG, Evals, and AI CI

pgvector + RAG-backed semantic search, eval harness in CI, and AI-assisted code review in the pipeline — finishes with a deployed demo

Academy Curriculum

Six weeks: a rigorous full-stack program followed by two weeks of production AI feature development on the same application.

Week1

Backend Language Foundations

5 full-days or 10 half-days · 35 training hours

Build a solid foundation in the chosen backend language—Java, Python, Node/TypeScript, C#, or Rust. Core syntax, object-oriented or functional patterns, data structures, exception handling, and an introduction to testing.

Topics

Language syntax, types, and control flow
OOP or functional design principles
Collections and generics
Exception handling and error modeling
I/O, streams, and async fundamentals
Unit testing basics (JUnit / pytest / Vitest / xUnit / cargo test)

Lab Project

Build a CLI task management application demonstrating CRUD, file persistence, and clean OOP/functional design with test coverage.

Week2

Backend API and PostgreSQL

5 full-days or 10 half-days · 35 training hours

Build production-grade RESTful APIs with the chosen framework and PostgreSQL. Authentication, authorization, database modeling, migrations, integration testing, and a CI pipeline.

Topics

REST API design and chosen framework (Spring Boot / FastAPI / Express / ASP.NET / Actix-web)
PostgreSQL with ORM or query builder
Database migrations
JWT authentication and RBAC
Unit and integration testing
CI pipeline with test gate

Lab Project

Build a RESTful task management API with user auth, RBAC, and full CRUD backed by PostgreSQL with CI.

Week3

Frontend: HTML, CSS, JS, and Chosen Framework

5 full-days or 10 half-days · 35 training hours

Master web fundamentals and the chosen frontend framework—React, Angular, Vue, or Svelte. Build a responsive UI that consumes the week 2 API.

Topics

Semantic HTML5 and accessibility
CSS Flexbox, Grid, and responsive design
Modern JavaScript/TypeScript (ES2025+)
Chosen framework: components, state, routing
API integration with Fetch/Axios
Frontend testing (Vitest / Jest / Cypress)

Lab Project

Build a responsive task dashboard consuming the week 2 API with the chosen framework, including auth flow and component tests.

Week4

Full-Stack Capstone: Containerized and Deployed

5 full-days or 10 half-days · 35 training hours

Integrate everything from weeks 1–3 into a complete, containerized, cloud-deployed application with a full CI/CD pipeline, monitoring, and a stakeholder presentation.

Topics

Docker multi-stage builds and Compose
Cloud deployment (customer-pickable: AWS / Azure / GCP)
CI/CD pipeline with deploy gate
Secrets management and environment config
Application monitoring and health checks
Final full-stack capstone presentation

Capstone

Ship a complete full-stack application—containerized, deployed, monitored, with CI/CD—presented to stakeholders on the final day of week 4.

Week5

AI Features Part 1: LLM Integration

5 full-days or 10 half-days · 35 training hours

This is where the program diverges from the standard Full-Stack Academy. Take the application from week 4 and add an LLM-powered feature using the Anthropic or OpenAI API: prompt design, structured outputs, streaming response UX, cost tracking, and token budget controls. The week is dedicated to making LLM integration production-quality before adding retrieval and evals on top.

Topics

Integrating the Claude or OpenAI API into the existing app
Prompt design and system prompts for product features
Structured outputs and reliable JSON
Streaming LLM responses in the chosen frontend framework
Error handling, retries, and graceful degradation
Cost tracking, per-user token budget controls, and rate limiting
Logging and observability for LLM calls

Week 5 Deliverable

The week 4 application upgraded with a production-quality LLM feature—streaming, structured outputs, and full cost/observability instrumentation—deployed to the cloud environment from week 4.

Week6

AI Features Part 2: RAG, Evals, and AI-Assisted CI

5 full-days or 10 half-days · 35 training hours

Build on the week 5 LLM feature with retrieval, evaluation, and AI-assisted CI. Add pgvector to the existing PostgreSQL database, design and implement a RAG-backed search experience, instrument the LLM feature with an eval harness that runs in CI, and add an AI-assisted code review step to the deploy pipeline. The week closes with a final stakeholder demo of the full AI-enhanced application.

Topics

Adding pgvector to the existing PostgreSQL database
Embedding strategies and chunking for the application's domain content
Building a RAG-backed semantic search feature end-to-end
Hybrid search and result quality tuning
Eval harness for the LLM and RAG features running in CI
AI-assisted code review step in the CI pipeline
Final stakeholder demo and Q&A

Final Deliverable

The week 5 application upgraded with RAG-backed search, an eval CI gate, and an AI-assisted code review step—deployed to cloud and presented to stakeholders on the final day. Graduates leave with a working AI-augmented full-stack application and the engineering practices to extend it.

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