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10 Weeks AI Product Engineering with TypeScript

Build the product system around LLMs: UI, backend, data, AI harness, streaming, tools, observability, and deployment in TypeScript.

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AI Product Engineering TypeScript Program flyer cover

Program Flyer

Download the AI Product Engineering flyer.

Download PDF ringkasan program ini untuk melihat arah pembelajaran, jalur product engineering, perjalanan kurikulum, profil mentor, harga, dan detail pendaftaran.

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The Problem

AI product engineering tidak berhenti di prompt.

Bagian tersulit dari AI app ada di area sekitar LLM: data flow, UI state, auth, validation, streaming, tool execution boundary, logging, deployment, dan cost control.

The Strategy

This program takes the product engineering path.

AI Engineering punya dua jalur besar. Model engineering fokus pada dataset, training, dan eval model. Program ini fokus pada product engineering: membuat LLM menjadi fitur produk yang bisa dipakai, dijaga, dan dikembangkan.

The System

Calling an LLM API hanya satu layer dari produk.

Student tidak langsung lompat ke inference. Mereka membangun fondasi produk lebih dulu, lalu menambahkan AI layer dengan kontrak, state, permission, observability, dan feedback loop.

Product System

The product system around the LLM.

Product Surface

Reactive UI, streaming states, generative interfaces, and user-facing flows.

Reliable Backend

Hono APIs, Zod contracts, durable objects, permissions, and workflow boundaries.

Data Layers

PostgreSQL, vector embeddings, user-owned data, and application context.

AI Harness

AI SDK, tools, observability, evals, cost control, and deployment infrastructure.

Curriculum

A 10-week journey from web foundation to reliable AI product.

Week 1

Typed Web Foundation

  • Modern JavaScript runtime and package management
  • TypeScript fundamentals, interfaces, generics, and type design
  • Vite, PNPM, linting, formatting, and Git workflow

Week 2

Reliable Backend with Hono

  • HTTP routing, handlers, middleware, and error boundaries
  • Zod validation and typed API contracts
  • Backend boundaries for frontend, data, and AI features

Week 3

Data Layers for AI Products

  • PostgreSQL schema design, relations, and migrations
  • Prisma queries, transactions, and maintainable data access
  • Data models for user content, AI outputs, and workflows

Week 4

Product Surface with React

  • React components, forms, routing, and state boundaries
  • TanStack Query for caching, mutations, and loading states
  • Responsive product screens while backend and AI work runs

Week 5

Product Readiness

  • Auth, sessions, permissions, and protected routes
  • File uploads, object storage, and user-owned resources
  • Env vars, deployment workflow, and basic observability

Week 6

AI Harness and SDK Integration

  • OpenAI SDK and provider-agnostic AI SDK patterns
  • Prompt design, structured output, validation, and fallbacks
  • LLM calls as product features, not prompt demos

Week 7

Streaming UI and Generative Interfaces

  • Streaming text from API routes into React UI
  • Chat UX, message state, optimistic UI, and recovery
  • Generative UI for summaries, drafts, copilots, and reviews

Week 8

Tools, Agents, and Workflow Boundaries

  • Tool design, input schemas, approval flow, and safe execution
  • Connect AI features to database and internal services
  • Agentic workflows with logs, retries, and user feedback

Week 9

Retrieval, Evaluation, and Reliability

  • Document ingestion, embeddings, retrieval, and RAG flow
  • Evaluation harnesses, test prompts, and regressions
  • Cost control, rate limits, model selection, and abuse prevention

Week 10

AI Product Engineering Final Project

  • Build a production-minded AI product in TypeScript
  • Present architecture, tradeoffs, and deployment decisions
  • Final assignment briefs

The order of the curriculum may change, but everything listed in the curriculum will be learned.

Outcomes

What students should be able to do after the program.

Typed Fullstack Skill

Build frontend, backend, API contracts, and database access in one coherent TypeScript workflow.

AI Product Engineering

Build product surfaces around LLMs with streaming UI, structured output, tool calling, and workflow boundaries.

Production Judgment

Understand auth, permissions, deployment, observability, cost control, and failure states for AI features.

Indra Zulfi Mushoddaq

Mentor

Indra Zulfi Mushoddaq

Interdisciplinary Software & AI Engineer

  • AI Engineering Lead @ AlphaX, Japan
  • Prev. Designstripe Canada, Generalist and Software Engineer
  • Prev. SSH International Bahrain, Design Team
indrazm.com

Scholarship Batch II

Informasi Penerima Scholarship

Empat penerima scholarship untuk Batch II telah terpilih. Lihat daftar lengkapnya di halaman scholarship.

Lihat penerima scholarship

Registration Open

IDR 1.799.000

Pembayaran bisa dilakukan 2 kali.

Dimulai

Starting Wednesday, 26 August 2026

Jadwal

Wednesday and Thursday 20:00 WIB

20 Live Class Sessions

2x 1-on-1 Mentoring

Auto Assignment Reviews

Lifetime Recording Access

Bonus Platform Course

Completion Certificate

Form Pendaftaran

Daftar AI Product Engineering Batch II

Isi data singkat. Setelah data terkirim, kamu bisa langsung melihat instruksi pembayaran.

Scholarship Closed

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Program Structure

Everything you need to know about the program structure.

Schedule

Wednesday and Thursday 20:00 WIB

Starting Wednesday, 26 August 2026

Duration

10 Weeks

2 live sessions per week

Prerequisites

Basic HTML, CSS, and JavaScript

TypeScript will be taught from the foundation

Format

100% Online Live Interactive Classes

Live class, assignment, review, and mentoring

Final Output

AI product engineering project

A deployable fullstack product for portfolio and review

Stack

React, Hono, PostgreSQL, TypeScript

With AI SDK, Zod, Prisma, and deployment workflow

Student Support

Support system to keep students moving.

Discord Community

Learn alongside Devscale students and alumni in a focused engineering community.

Assignment Feedback

Submit work, receive feedback, and improve through practical iteration instead of passive watching.

Mentor Guidance

Use 1-on-1 sessions to discuss technical blockers, project direction, and career context.

Devscale Direction

Why this direction fits Devscale.

One Language Across the Product

TypeScript lets students share types, schemas, and product assumptions across frontend, backend, data, and AI harness code.

Built for AI Product Engineering

The curriculum focuses on the hard parts around the model: UI state, streaming, tools, data access, auth, and deployment.

Modern Web Stack

Students work with React, Hono, PostgreSQL, Prisma, Zod, TanStack Query, and AI SDK patterns used in current product teams.

Mentor-Led Feedback

Live classes, assignments, and 1-on-1 sessions give students direct feedback on architecture, code quality, and product decisions.

Related Reading

The thinking behind this program direction.

All Posts

Career direction

Two Paths for AI Engineers

Kenapa program ini fokus ke LLM product harness, bukan model training dari nol.

Read the article

Program direction

Menutup Program AI-Enabled Python Web Development

Alasan Devscale mengalihkan fokus program publik dari Python ke full-stack TypeScript.

Read the article

FAQ

Common questions before joining the program.

Apakah program ini menggantikan AI-Enabled Python? ⌃

Untuk program publik Devscale, iya. AI-Enabled Python kami sunset agar fokus kurikulum berikutnya lebih tajam ke AI Product Engineering dengan TypeScript. Python tetap bagus dan tetap relevan, terutama untuk data, automation, machine learning, dan eksperimen model. Namun untuk membangun produk web modern dengan UI, backend, database, auth, streaming, dan AI feature dalam satu workflow, kami memilih TypeScript sebagai fokus utama.

Apakah saya harus sudah menguasai TypeScript? ⌃

Tidak harus. Kamu perlu nyaman dengan dasar HTML, CSS, dan JavaScript terlebih dahulu. TypeScript akan dipelajari dari fondasi, mulai dari type dasar, interface, narrowing, generics, sampai cara memakai type untuk menjaga API contract, schema, dan data flow di aplikasi fullstack.

Apakah program ini cocok untuk pemula? ⌃

Cocok untuk beginner yang sudah punya dasar web development. Program ini bukan mulai dari nol absolut, jadi kamu sebaiknya sudah memahami struktur HTML, styling CSS, JavaScript dasar, function, array, object, dan async dasar. Jika belum, kamu tetap bisa mendaftar, tapi kami sarankan menyelesaikan materi dasar web terlebih dahulu sebelum batch dimulai.

Apakah pembayaran bisa 2x? ⌃

Program ini biasanya tersedia dengan opsi pembayaran 2x. Informasi lebih lanjut akan diumumkan saat pendaftaran batch berikutnya dibuka.

Apakah akan belajar AI model training? ⌃

Tidak. Fokus program ini bukan melatih model dari nol. Fokusnya adalah membangun produk yang mengintegrasikan AI secara utuh: memakai LLM API, AI SDK, structured output, streaming UI, tool calling, workflow automation, retrieval, evaluation sederhana, dan production safety. Jadi arahnya adalah AI Product Engineering, bukan machine learning research.

Apa final project yang akan dibuat? ⌃

Final project berupa aplikasi fullstack TypeScript yang punya fitur AI nyata. Student akan membangun product surface untuk single-turn AI interaction, multi-turn agent experience, agentic workflow dengan tool calling, RAG atau retrieval layer, serta observability untuk melihat trace, logs, error, dan kualitas output. Yang dinilai bukan hanya AI-nya, tapi juga arsitektur aplikasi, database design, UX, auth, deployment, error handling, dan keputusan teknisnya.