AI-Assisted Development
From Junior Prompt-User to Senior AI Orchestrator
AI writes a growing share of your codebase — faster than most engineers can responsibly review. This cohort makes you the engineer who directs it, audits it, and owns every line. Ship faster and sleep at night.
AI now writes a meaningful fraction of production code, and it does so faster than most engineers can responsibly absorb. The result is a new and growing category of technical debt: the silent N+1 query, the missing idempotency key, the JWT signed with the wrong algorithm — all of it plausible, working-looking, and accepted by an engineer who could not evaluate it.
This course exists to solve that problem. Its thesis is simple: the way to direct AI well is to know more than it does about the thing you are asking for. A senior engineer gets good output from AI because they can specify precisely, recognise what is wrong, and demand the fix. This course makes the student that engineer.
Crucially, the engineering fundamentals taught here are not a separate track from 'AI orchestration' — they are the orchestration skill. SOLID, complexity analysis, testing, API design, resilience, observability, and security are presented as the exact lens used to audit AI output and the exact vocabulary used to demand better.
The course moves from foundation, through engineering fundamentals and distributed-systems concerns, to a full lifecycle: the student directs AI to build two complete, architecturally distinct production applications — then audits, hardens, chaos-tests, and defends them.
AI rarely writes *bad* code — it writes plausible, working-looking code. The problem is the silent debt that comes with it: the N+1 query, the missing idempotency key, the JWT signed with the wrong algorithm, the if/elif that should have been a strategy. An engineer who can't evaluate that code ships it. Multiply that across a team at AI speed, and you get a codebase nobody fully understands.
The fix isn't better prompts. It's knowing enough to direct AI — to specify precisely, recognize what's wrong, and demand the fix.
Junior prompt-user
"Write me a function that does X." — takes what AI gives
Senior AI orchestrator
"Here's the spec, constraints, and test strategy. Generate it, then justify your trade-offs." — owns the result
- 1A permanent mindset shift: from taking AI output to directing it — specify precisely, audit ruthlessly, demand production-grade work
- 2Full-stack engineering fluency (SOLID, Big-O, testing, API design, resilience, observability, security) as the vocabulary to audit AI and demand better
- 3Two deployed, defended production applications — a banking monolith and a healthcare microservices system — built, audited, load-tested, and chaos-tested
- 4A reusable orchestration toolkit: AGENTS.md/skills.md templates, a prompt library, a code-review checklist, an incident post-mortem template
You build and ship two architecturally distinct systems end to end — then audit, harden, and defend each.
| Domain | Architecture | Key Technologies |
|---|---|---|
| Banking | Monolith | JWT auth, PostgreSQL, Redis, Docker, Kubernetes |
| Healthcare | Microservices | gRPC, Kafka, Saga pattern, OpenTelemetry, mTLS, HIPAA/GDPR-aware design |
17 sessions · 34h across 4pillars · every session ends with a working, reviewable artifact. Expand any pillar for the session lineup.
Pillar 1 — The FoundationMindset, Requirements & Anti-Patterns · Weeks 1–2▼
- Session 1 — The Senior Mindset & Requirements Engineering — Turn vague asks into specs AI can't misinterpret: acceptance criteria, NFRs, and saying 'no' to feature creep.
- Session 2 — Tool Setup & AI Orchestration — Set up Claude Code, Antigravity, Cursor, and Copilot, and build an AI 'team' that enforces your standards.
- Session 3 — Prompt & Context Engineering for Production — System, few-shot, and negative prompting, plus context management that produces production-ready code.
- Session 4 — Common Code Anti-Patterns (The AI Trap) — The five anti-patterns AI reaches for by default, and how to catch them before they ship.
Pillar 2 — Engineering FundamentalsOOP, Algorithms, Testing & API Design · Weeks 3–4▼
- Session 5 — OOP, SOLID, Clean Code & Design Patterns — Use SOLID as an audit lens, and learn when applying a pattern is itself the mistake.
- Session 6 — Algorithms, Big-O & Performance Profiling — Catch the O(n²) AI loves to write, and profile before you optimize.
- Session 7 — Testing Fundamentals & Mutation Testing — Write tests that actually catch bugs, and use mutation testing to expose the ones that don't.
- Session 8 — API Paradigms & Cloud-Native Architecture — Choose between REST, gRPC, and GraphQL on purpose, and avoid the distributed monolith.
Pillar 3 — Distributed Resilience, Observability & SecurityCircuit breakers, retries, tracing, containers, OWASP · Weeks 5–7▼
- Session 9 — Resilience Patterns: Circuit Breakers & Retries — Make AI's happy-path code survive real dependency failures.
- Session 10 — Distributed Transactions: Idempotency & Sagas — Move money and data across services without double-spends or corruption.
- Session 11 — Observability, Metrics & Distributed Tracing — Structured logs, the RED method, and traces you can actually debug with.
- Session 12 — Containerization & Cloud-Native Deployment — Multi-stage Docker and Kubernetes with autoscaling.
- Session 13 — Security Deep-Dive: Auth, JWT & OWASP — JWT done right, the OWASP Top 10 mapped to AI's common mistakes, and a security-audit checklist.
Pillar 4 — Full Lifecycle ExecutionBuilding the Two Heavy Projects · Weeks 7–8.5▼
- Session 14 — Project 1 (Banking): Build, DB Performance & Caching — Complete banking app with JWT auth, database performance (N+1 elimination, indexing), and Redis cache-aside.
- Session 15 — Project 1 (Banking): Formal Audit, Load-Testing & Deploy — Audit against the anti-pattern and security checklists, load-test with k6, deploy to Kubernetes.
- Session 16 — Project 2 (Healthcare): Event-Driven Distributed Build & Compliance — Microservices with gRPC, Kafka, appointment-booking Saga, mTLS, and HIPAA/GDPR-aware data design.
- Session 17 — Project 2 Validation (Chaos, Incident Response & Post-Mortem), Capstone & Playbook — Chaos-test the distributed system, live incident response loop, blameless post-mortem, and capstone defense.
Want the full session-by-session syllabus with objectives and deliverables? Request it here— we'll send it over.
| Delivery | Cohort-based, live, instructor-led |
| Class length | 2 hours per class |
| Cadence | 2 classes/week (4 hours/week) |
| Total length | 17 sessions · 34h over 8.5 weeks |
| Location | Dhaka + Global (remote seats available) |
| Prerequisites | Comfort reading and writing code in at least one language. No prior AI-tooling experience required — Session 2 sets up the tools from scratch. Comfort with abstract systems thinking is expected from Pillar 3 onward. |
This course does not cover model fine-tuning or training, deep ML theory, data-warehouse engineering, full platform engineering (Terraform/cloud-account design), or polished frontend development. It teaches engineers to direct AI across the software delivery lifecycle and to own the result — not to replace specialists in those adjacent fields.
৳9,000
৳4,500
$69 global
Early-bird price ends 1 August
First cohort starts Friday, 01 August 2026
Enrollment closes 1 August
Do I need prior experience with Claude Code, Cursor, or Copilot?▼
No. Session 2 sets up all four tools from scratch and covers what each is best at.
Will I actually deploy anything, or is this all theory?▼
Both projects are built and deployed — Project 1 to Kubernetes, Project 2 as a full microservices stack with mTLS between services. You leave with two live deployments, not slides.
What if I miss a live class?▼
Cohort sessions are recorded; the hands-on exercises are designed to be completed either way.
Is this part of a longer track?▼
It's a standalone course. To go deeper afterward, see Agentic AI and AI Systems Design in the Engineering Track.
Part of a longer track?
This course is part of the TIER A · ENGINEERING. Explore the full catalog to find related courses that build on each other.
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