AI BootCamps (6 Courses)
Career Oriented
Course Category Career Oriented
Course Category 
Google Rating
Professionals Upskilled
Live Cohorts Delivered
Enterprise Teams Trained
Full-stack developers are expected to do everything—frontend, backend, APIs, debugging, databases, testing, performance, and security. This course shows how AI fits into the entire development lifecycle and helps reduce the workload that slows teams down.
You will learn practical AI workflows for writing code, reviewing pull requests, generating tests, solving bugs, optimizing systems, and documenting projects. By the end, you'll know how to work alongside AI instead of treating it like just another autocomplete tool.
100% Live instructor-led sessions focused on real development scenarios
Hands-on learning with ChatGPT, Claude, GitHub Copilot, Cursor, and AI developer tools
Learn debugging, API design, testing, architecture, and security workflows
Understand how Agentic AI can automate repetitive development work
Build a reusable AI workflow system for your day-to-day projects
Gain hands-on experience through realistic development capstones
Learning Objective:
Use AI to write better code faster — from boilerplate to complex logic — without compromising quality, understanding, or ownership.
Topics:
Learning Objective:
Use AI to raise code quality consistently — catching bugs, security issues, and design problems before they reach production.
Topics:
Learning Objective:
Use AI to design consistent, well-documented APIs that developers enjoy consuming — faster than manual design processes allow.
Topics:
Learning Objective:
Use AI to write better tests faster — achieving higher coverage with less effort and catching edge cases that manual test writing misses.
Topics:
Learning Objective:
Use AI to design efficient database schemas, write optimised queries, and resolve performance problems faster than manual analysis allows.
Topics:
Learning Objective:
Use AI to diagnose and resolve bugs faster — turning hours of manual debugging into minutes of focused, systematic investigation.
Topics:
Learning Objective:
Use AI to design scalable, maintainable system architectures — evaluating options, documenting decisions, and anticipating future problems.
Topics:
Learning Objective:
Use AI to build more resilient systems — designing comprehensive error handling, retry logic, and failure recovery patterns.
Topics:
Learning Objective:
Use AI to create the technical documentation that enables teams to understand, maintain, and extend systems confidently without the original author present.
Topics:
Learning Objective:
Use AI to identify performance bottlenecks, design scalable solutions, and make evidence-based optimisation decisions.
Topics:
Learning Objective:
Use AI to write more secure code by default — catching vulnerabilities early and applying security best practices systematically.
Topics:
Learning Objective:
Apply every skill from the course to a realistic end-to-end full stack development scenario across design, implementation, testing, and operations.
Capstone Project 1:
API design specification for a defined set of business requirements
Capstone Project 2:
Complete implementation with AI-generated code, tests, and inline documentation
Capstone Project 3:
Database schema design and query optimisation for a data-intensive scenario
Capstone Project 4:
Debugging a simulated production issue from logs and error traces
Capstone Project 5:
Architecture design for a defined scalability challenge
Capstone Project 6:
Personal 90-day AI adoption roadmap for your development practice
To fast-track your career and achieve
There is no exam for this program.
This course transformed the way I manage projects. I’m finally ahead of the game.
The questions, tools, and case studies all felt tailor-made for a developer like myself. Agilefever has helped me manage smoother sprints and more effective meetings.
I was hesitant about AI. Now I can’t work without it. AgileFever made Gen AI and Agentic AI simple, useful, and surprisingly fun to learn.
One of the most common developer discussions online. AI is speeding up development, but teams still need developers who understand architecture, business logic, debugging, and decision-making.
No. The course is designed for developers at different stages who want to learn practical AI workflows.
Yes. AI helps accelerate development, but developers still review, improve, and own what gets built.
The course focuses on concepts and AI workflows that work across modern stacks and languages.
Yes. AI can analyze stack traces, logs, code patterns, and help narrow down root causes significantly faster.
Yes. The course covers the complete full stack lifecycle including APIs, databases, testing, security, and architecture.
Both. You learn practical tools and also understand when, why, and how to use AI effectively.
Yes. You complete six hands-on capstone projects based on realistic development scenarios.