Master AI Tools
Navigate 25+ AI Tools — From Requirements to Production Monitoring
AI Tools
Ecosystems
SDLC Phases
Real Features
The Problem Every Developer Faces Today
AI tools for software development are multiplying fast — and every team is adopting them differently. The result: developers who can use one tool but cannot navigate the ecosystem. They do not know which AI assistant is best for which phase of development, which tools actually save time versus create friction, or how to compare Claude Code versus GitHub Copilot versus Cursor versus Gemini CLI.
"The real skill gap isn't coding. It's knowing which AI tool to reach for — and when."
GitHub's 2024 research found developers complete tasks up to 55% faster with AI coding assistants — and that covers only the development phase. This program covers all nine SDLC phases.
What You'll Build: LearnFlow LMS
A complete, production-ready Learning Management System — from scratch, end-to-end, using four different AI ecosystems.
12 Features Shipped
12 full-featured use cases across 4 AI ecosystems
Full Stack (Python + React + DB)
Python 3.13 + FastAPI + React 19 + PostgreSQL 17
Portfolio: Real project w/ tests & CI/CD
Real project with tests, docs, CI/CD, and monitoring
| You Run This | AI Generates This |
|---|---|
| Python 3.13 + FastAPI + SQLAlchemy | All feature code, endpoints, models |
| React 19 + TypeScript + Tailwind CSS 4 | Components, pages, API integration |
| PostgreSQL 17 + JWT auth | Migrations, schemas, auth logic |
| Docker + GitHub Actions | Dockerfiles, CI/CD YAML, compose configs |
| Render / Fly.io (hosting) | pytest suites, Playwright E2E, API docs |
Who Should Attend
This program is designed for technical professionals who want to master AI-assisted development across the full software lifecycle.
Software Developers
Level up with AI tools your team is already adopting
Tech Leads & Architects
Evaluate AI tool choices with hands-on evidence
DevOps / Platform Engineers
AI-generated pipelines, security scans, monitoring
QA Engineers
AI-generated test cases, edge case detection, E2E automation
Engineering Managers
Understand ROI and trade-offs of each AI ecosystem
Security Engineers
AI-powered SAST, SCA, and vulnerability remediation guidance integrated into CI/CD
Four Ecosystems. One Project. Every Phase.
Compare 4 complete AI development ecosystems side-by-side. Each ecosystem covers all 9 SDLC phases using exclusively free-tier tools.
| SDLC Phase | AI Tool | What the AI Does | Free Tier |
|---|---|---|---|
| Requirements | Claude.ai | Generate user stories, acceptance criteria, BRD sections | ✅ |
| Requirements | Fathom | AI transcription + semantic action-item extraction from stakeholder calls | ✅ |
| Design | v0 (Vercel) | Generate React UI components from natural language prompts | ✅ |
| Design | Uizard | Generate wireframes from text descriptions or screenshots | ✅ |
| Development | Claude Code | Terminal AI agent — plans features, edits files, runs commands, manages git | ✅ |
| Code Review | CodeRabbit | Proactive AI review on every PR with full codebase context | ✅ |
| Testing | Qodo | AI suggests missing edge cases and test scenarios | ✅ |
| Security | Snyk | AI-powered SAST, SCA, fix prioritization, remediation guidance | ✅ |
| Documentation | Mintlify | AI semantic search over API docs; auto-generates OpenAPI portal | ✅ |
| CI/CD | Claude Code | Generates GitHub Actions YAML from plain English pipeline description | ✅ |
| Monitoring | Datadog | Watchdog (ML anomaly detection) + AI Assistant (NL investigation queries) | ✅ |
Lab Workflow
- 1Claude.ai → draft user stories from a feature brief
- 2Fathom → record and summarize stakeholder call
- 3v0 → generate React component from UI description
- 4Uizard → wireframe the user flow
- 5Claude Code → implement the feature end-to-end
- 6Claude Code → generate tests; CodeRabbit reviews the PR
- 7Snyk → scan for vulnerabilities; Claude Code fixes them
- 8Claude Code → generate docstrings and README section
- 9Claude Code → generate CI/CD YAML; GitHub Actions runs it
- 10Datadog → set up Watchdog alert; query with AI Assistant
Use ← → arrow keys to navigate between ecosystems
12 Features Built with AI
Every use case is a complete, production-ready feature built with a distinct AI ecosystem.
| Use Case | Feature | Tool Set |
|---|---|---|
| UC-1 | User Registration & Authentication | Set A |
| UC-2 | Course Creation & Management | Set B |
| UC-3 | Student Enrollment | Set C |
| UC-4 | Quiz & Assessment Engine | Set D |
| UC-5 | Lesson Delivery & Progress Tracking | Set A |
| UC-6 | Analytics Dashboard | Set B |
| UC-7 | Notifications | Set C |
| UC-8 | Certificate Generation | Set D |
| UC-9 | Course Reviews & Ratings | Set A |
| UC-10 | Search & Filter | Set B |
| UC-11 | User Profile Management | Set C |
| UC-12 | Admin Panel | Set D |
Final deliverable: A deployed, tested, documented LMS with CI/CD and monitoring — built using four distinct AI ecosystems.
What Makes This Program Different
Four Ecosystems, Not One
Most programs teach one tool stack and call it done. You leave knowing four ecosystems and how to make a reasoned choice between them.
Full SDLC, Not Just Coding
Requirements → Design → Development → Code Review → Testing → Security → Documentation → CI/CD → Monitoring. Every phase. Every use case.
Free Tiers for Labs
Every AI tool used in hands-on labs has a free tier, verified as of February 2026.
Real Software, Not Exercises
LearnFlow is a real application with a real database, real authentication, real tests, and a real deployment. Your portfolio project has substance.
The Distinction That Matters
AI tools generate. Platform tools execute. You will learn to tell the difference and articulate it clearly to your team. pytest runs tests — Claude Code writes them. GitHub Actions runs pipelines — Copilot generates the YAML.
Learning Outcomes
Upon completing this program, participants will be able to:
- Use AI tools at every phase of the SDLC — not just code completion
- Compare and evaluate four major AI development ecosystems with hands-on evidence
- Generate requirements docs, wireframes, tests, security fixes, API docs, and CI/CD pipelines using AI
- Distinguish AI-powered tools from rule-based tools and articulate the difference to a team
- Build and ship a full-stack application with AI-assisted development practices
- Set up AI-powered production monitoring with anomaly detection
Program Schedule
- Format
- Instructor-led, hands-on labs
- Delivery
- Virtual (live, instructor-led)
- Duration
- 16 weeks (one session per week)
- Session
- Every Saturday, 2 hours
- Lab environment
- Personal laptop (all tools on free tiers) or GitHub Codespaces
- Languages
- Python 3.13 (backend) · TypeScript / React 19 (frontend)
Session Time by Timezone
| Region | Timezone | Session Time |
|---|---|---|
| India | IST (UTC+5:30) | Saturday 7:00 PM – 9:00 PM |
| USA (East Coast) | EST (UTC-5) | Saturday 8:30 AM – 10:30 AM |
| UK / Europe | GMT (UTC+0) | Saturday 1:30 PM – 3:30 PM |
| UAE / Middle East | GST (UTC+4) | Saturday 5:30 PM – 7:30 PM |
| Singapore / East Asia | SGT (UTC+8) | Saturday 9:30 PM – 11:30 PM |
Simple, Transparent Pricing
One-time fee. No hidden charges. Full program access from day one.
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Master AI Tools
one-time payment
- 25+ AI tools across 4 ecosystems
- Full SDLC coverage — not just coding
- Build a real LMS as portfolio project
- Saturday live sessions
- Certificate on completion
Ready to Navigate the AI Ecosystem?
Join the next cohort — Saturday sessions, free tier tools, real software.
Rathinam Trainers & Consultants Private Limited
All AI tools used in labs have verified free tiers as of February 2026.