M01-03 · AI-Assisted Software Development

Feature Development — From Design Doc to Production

Teaches students to own a feature from inception through deployment and post-deploy monitoring. Covers writing technical design documents, effort estimation, implementing features across the full stack (UI, API, database), handling database migrations, diagnosing performance issues, and verifying features in production. Students learn the complete feature lifecycle that defines a productive mid-level developer.

35 Hours

8 Learning objectives

Evaluate Bloom's ceiling (?)

5 Competencies

What You'll Be Able To Do

Learning Objectives

Objectives

Depth

Write technical design documents that specify what a feature does, how it will be built, alternatives considered, and anticipated risks — reviewed before implementation begins Create
Apply CORE-05 estimation frameworks to software feature development, accounting for technical unknowns (unfamiliar codebases, third-party API dependencies), integration complexity, and the higher variance inherent in software estimation Apply
Implement a multi-component feature across frontend (React), API layer, and database, maintaining consistency across layers Apply
Write and execute database migrations safely: schema changes, data transformations, rollback strategies, and staging-to-production promotion Apply
Diagnose and resolve performance issues using EXPLAIN plans, database indexing, N+1 query detection, and profiling tools Analyze
Deploy features through CI/CD with staged rollout: feature flags, staging verification, production monitoring post-deploy Apply
Use AI tools to accelerate feature development (scaffolding endpoints, generating boilerplate, type definitions) while reviewing for edge cases AI typically misses Evaluate
Coordinate feature work across sprint cycles: participate in sprint planning, provide accurate status updates, and communicate blockers early Apply

Levels: Remember · Understand · Apply · Analyze · Evaluate · Create — highest demands most original thinking.

Core Competencies

What You'll Master

Technical Design

Design documents, architecture decisions for features, alternative analysis, risk identification.

Full-Stack Feature Implementation

Building features across frontend, API, and database layers with consistent data flow.

Database Operations

Schema migrations, data transformations, indexing strategies, query optimization.

Performance Diagnosis

EXPLAIN plans, N+1 detection, profiling, optimization strategies.

Effort Estimation

Breaking features into estimable units, calibrating estimates against actuals, communicating timeline risks.

Portfolio Artifact

What You'll Build

Feature Ship Case Study — Student takes a product requirement ("Add Export to CSV on analytics dashboard" or equivalent), writes a technical design document, implements the feature across frontend and backend (including database changes), handles edge cases (large datasets, special characters, concurrent access), deploys through CI/CD, and writes a post-deploy monitoring report. Includes estimated vs. actual hours with a calibration reflection.

Tools You'll Use

Industry Tools, Not Toy Projects

VS Code / Cursor

Professional code editors with AI-assisted development, debugging, and integrated terminal.

Claude Code & GitHub Copilot

AI assistants for scaffolding endpoints, generating boilerplate, and reviewing edge cases.

React / Next.js

Frontend framework for building interactive user interfaces and full-stack web applications.

Node.js / Express

Backend runtime and framework for building API endpoints and server-side logic.

Prisma / TypeORM

Database ORMs for schema management, migrations, and type-safe database queries.

PostgreSQL

Production-grade relational database with EXPLAIN plans, indexing, and performance tuning.

Vercel / AWS

Deployment platforms for staging and production environments with CI/CD integration.

Datadog / Sentry

Application monitoring and error tracking for post-deploy verification and performance analysis.