AI-AugmentedFull-StackSoftware Engineering
A practical engineering workflow for building, testing, securing, and deploying software with AI.
- Modules
- 16
- Capstone
- 01
- Lessons
- 148
- Format
- Self-paced
You are not learning how to make AI write code for you.
You are learning how to use AI as an engineering partner while retaining technical understanding, decision-making, verification, and accountability.
- 01AI proposes.
- 02The developer decides.
- 03The system verifies.
How to work as an AI-augmented software engineer.
What this course is not
This is not an introduction to programming. Foundational concepts are explained only where a decision depends on them.
- What is frontend?
- What is backend?
- What is a database?
What this course is
How to work as an AI-augmented software engineer.
- 01AI collaboration
- 02Context engineering
- 03Task decomposition
- 04Requirements engineering
- 05Planning
- 06Architecture
- 07Implementation
- 08Debugging
- 09Testing
- 10Verification
- 11Security
- 12Code review
- 13Git workflow
- 14Deployment
- 15Operations
- 16Engineering judgment
Sixteen modules. One capstone.
148 lessons, ordered so that every module depends only on what came before it.
01The AI-Augmented EngineerWhat changes when AI enters the workflow, what does not, and where responsibility still sits.7 lessons
Structural base
- 01Welcome to AI-Augmented Engineering
- 02From Coding to Engineering
- 03AI-Augmented vs AI-Dependent
- 04What AI Is Good At
- 05Where AI Commonly Fails
- 06Human Responsibility in AI-Assisted Development
- 07The SanX AI Engineering Loop
02Working With AI as an Engineering PartnerContext engineering, decomposition and structured collaboration — the skills that separate direction from guessing.8 lessons
Interconnected systems
- 01AI Is Not a Search Box
- 02Task Decomposition
- 03Engineering Requests
- 04Context Engineering
- 05Repository Context
- 06Asking AI to Ask Questions
- 07Structured AI Outputs
- 08Multi-Pass Collaboration
03Problem Discovery With AIFinding the real problem, mapping assumptions, and verifying what AI tells you before you build on it.8 lessons
Exploration
- 01Start With the Problem
- 02AI-Assisted Problem Analysis
- 03User and Stakeholder Discovery
- 04Assumption Mapping
- 05Edge-Case Discovery
- 06Research With AI
- 07Source Verification
- 08Problem Validation
04AI-Assisted Requirements EngineeringTurning an idea into specifications precise enough that correctness becomes checkable.9 lessons
Organized components
- 01From Idea to Requirements
- 02Feature Decomposition
- 03User Stories
- 04Acceptance Criteria
- 05Business Rules
- 06Constraints
- 07Ambiguity Detection
- 08Failure States
- 09AI Requirements Review
05AI-Assisted Engineering PlanningSequencing work into small changes, mapping dependencies, and deciding what AI should never own.9 lessons
Blueprint
- 01Requirements to Engineering Tasks
- 02Breaking Work Into Small Changes
- 03Dependency Mapping
- 04Technical Risk Identification
- 05Choosing What AI Should Do
- 06Choosing What Requires Human Judgment
- 07Implementation Order
- 08Definition of Done
- 09Development Roadmap
06AI-Assisted System ArchitectureGenerating alternatives, interrogating trade-offs, and using AI as an architecture critic instead of an oracle.9 lessons
Layered structure
- 01Requirements to Architecture
- 02Generate Architecture Alternatives
- 03Architecture Trade-Offs
- 04AI as Architecture Critic
- 05Finding Overengineering
- 06Architecture Decision Records
- 07System Boundaries
- 08Failure and Recovery Thinking
- 09Architecture Review
07AI-Assisted Data & API DesignModelling data and designing contracts first, so implementation has something to be correct against.9 lessons
Connected structured elements
- 01Data Modeling With AI
- 02Entities and Relationships
- 03Constraints and Validation
- 04Schema Review
- 05API Contract Design
- 06Request and Response Contracts
- 07Error Contracts
- 08AI Contract Review
- 09Contract-First Implementation
08AI-Assisted ImplementationThe small-change principle, reading every diff, and keeping ownership of code you did not type.10 lessons
Assembly
- 01The Small-Change Principle
- 02Implement From the Plan
- 03AI Code Generation
- 04Giving AI Existing Patterns
- 05One Feature at a Time
- 06Reviewing the Diff
- 07Running Immediately
- 08Commit After Verification
- 09Rejecting Bad AI Changes
- 10Maintaining Code Ownership
09AI-Assisted DebuggingHypothesis-driven diagnosis with real runtime evidence, instead of re-prompting until the error disappears.10 lessons
Inspection
- 01Diagnose Before Fixing
- 02Creating Debugging Context
- 03Reproduction Steps
- 04Hypothesis-Driven Debugging
- 05Reading Errors With AI
- 06Logs and Runtime Evidence
- 07Root Cause Analysis
- 08Evaluating AI Fixes
- 09Regression Prevention
- 10Debugging Loops
10AI-Assisted Testing & VerificationThe core discipline: AI generates, but only verification decides what ships.12 lessons
Controlled stress
- 01Generation vs Verification
- 02Requirements to Tests
- 03AI-Generated Unit Tests
- 04Integration Tests
- 05End-to-End Tests
- 06Negative Testing
- 07Edge-Case Generation
- 08AI as an Adversarial Tester
- 09Static Analysis and Type Checking
- 10Automated Quality Gates
- 11Human Verification
- 12Definition of Done
11AI-Assisted Security EngineeringThreat modelling, attack surface, and the specific security mistakes AI-generated code keeps making.11 lessons
Boundaries
- 01Security Before Deployment
- 02Threat Modeling With AI
- 03Attack Surface Discovery
- 04Input Validation
- 05Authentication and Authorization Review
- 06Secrets and Configuration
- 07Dependency Risk
- 08AI-Generated Security Mistakes
- 09AI as an Attacker
- 10AI Application Security
- 11Security Verification
12AI-Assisted Code Review & RefactoringReviewing for correctness, maintainability, security and performance — and detecting AI slop.10 lessons
Refinement
- 01What Makes Code Good?
- 02AI Code Review
- 03Correctness Review
- 04Maintainability Review
- 05Security Review
- 06Performance Review
- 07Detecting AI Slop
- 08Safe Refactoring
- 09Refactor → Test → Compare
- 10Technical Debt
13AI-Augmented Git & CollaborationGit as the safety net that makes aggressive AI assistance survivable, plus working with other engineers.9 lessons
Branches converging
- 01Git as an Engineering Safety Net
- 02Small Meaningful Commits
- 03AI-Assisted Commit Messages
- 04AI-Assisted Pull Requests
- 05AI-Assisted Code Review
- 06Responding to Review Feedback
- 07Repository Instructions for AI
- 08AI-Assisted Documentation
- 09Working in Existing Codebases
14AI-Assisted Deployment & DevOpsGetting software into production safely: environments, containers, pipelines and incident response.10 lessons
System moving into production
- 01Deployment Planning
- 02Environment Configuration
- 03AI-Assisted Docker
- 04AI-Assisted CI/CD
- 05Deployment Troubleshooting
- 06Production Logs
- 07Monitoring
- 08Error Investigation
- 09Safe Production Changes
- 10Incident Response With AI
15AI-Augmented Operations & Continuous ImprovementObservability as feedback, and using production reality to drive what gets built next.8 lessons
Live system
- 01Observability as Feedback
- 02AI-Assisted Production Analysis
- 03Performance Investigation
- 04Technical Debt Detection
- 05Maintenance Planning
- 06AI-Assisted Release Planning
- 07Post-Incident Analysis
- 08Continuous Improvement
16Engineering Judgment in the AI EraWhen not to use AI, how to notice it is wrong, and how to avoid quietly losing your own capability.9 lessons
Branching decision structure
- 01When Not to Use AI
- 02When AI Should Not Make the Decision
- 03Knowing When AI Is Wrong
- 04AI Overengineering
- 05Maintaining Technical Understanding
- 06Avoiding AI Dependency
- 07Measuring AI Productivity
- 08Building a Personal AI Engineering Workflow
- 09The AI Engineering Playbook
One loop. Every feature. Every time.
The workflow you will run hundreds of times by the end of the course. It is deliberately unglamorous — that is the point.
01 / 10
Understand
Know the problem before touching a prompt.
Know the problem before touching a prompt.
Don’t just finish a course. Ship something.
You take one software idea through the entire lifecycle. Not a tutorial repository — a real, working, deployed product you can defend in an interview.
What you deliver
Six components. One system.
An AI instructor delivers the lesson, but delivery is the easy part. What makes the Academy work is everything that verifies you actually learned it.
AI Instructor
A speaking instructor delivers each lesson with technical demonstrations.
AI Mentor
Context-aware assistance while you work — it asks questions before giving answers.
Practical Exercise
Every lesson ends in a hands-on task against a real codebase.
Evaluation
Checkpoints confirm understanding before the path continues.
Capstone
One product, taken end to end, judged as an engineering deliverable.
Certificate
Issued only on full completion of lessons, exercises, checkpoints and capstone.
Earned, not attended.
The SanX Academy certificate is issued on verified completion. It is a record of work you did, not a receipt for a purchase.
- 01All lessons completed
- 02All exercises submitted
- 03All checkpoints passed
- 04Capstone product delivered
- 05Final assessment passed
SanX Academy issues its own certificate of completion. It is not an externally accredited qualification.
SanX Academy
AI-Augmented Full-Stack Software Engineering
Certificate of Completion
Learn the workflow. Keep the judgement.
A practical engineering workflow for building, testing, securing, and deploying software with AI.