Skip to main content

3.0 AI-Augmented Features & Systems Thinking

Overview​

This module deepens complexity by expanding the product from the Foundations Module and introducing AI integration and multi-component systems. The focus is on systems thinking — understanding how to incorporate AI capabilities and additional modules into a product — while still letting AI do the heavy coding.

Core Themes:

  • Expanding a product with major new functionality
  • Integrating AI services into applications
  • Multi-component architecture and API orchestration
  • Increased autonomy in deciding what to build

What You'll Do​

You'll add a major new AI feature to the Pet Clinic application. This isn't fixing bugs — it's designing and implementing significant new functionality that spans multiple components.

Example features (choose one or propose your own):

  • AI Assistant — A chatbot that answers questions about pets, appointments, or clinic services
  • Scheduling System — Calendar-based appointment booking with conflict detection
  • User Portal — Self-service interface for pet owners
  • Analytics Dashboard — Insights into clinic operations, visit patterns, etc.

Feature Expectation: Feature touches multiple services (backend, db, etc). Feature integrates LLM or related technology (MPC, Agents, etc). You used LLMs to go from idea to feature; planning, task breakdown, phased implementation.

Primary Capabilities You'll Develop​

Integrating AI Features​

Learn how to embed AI services into a product:

  • Calling AI model APIs from your application
  • Handling prompts and responses in an app context
  • Dealing with AI-specific challenges (context management, latency, error handling)
  • Building useful AI-powered user experiences

Multi-Module Architecture & API Orchestration​

Move beyond single features to system design:

  • Architecting with AI support — generating boilerplate, stubbing APIs
  • Designing how components connect and communicate
  • Managing data flow across services
  • Considering system impacts (security, performance, user experience)

Advanced Prompting & AI Code Reviews​

As projects grow, refine your techniques:

  • Modifying existing code safely with AI
  • Generating code that fits into an existing architecture
  • Using AI for code review and documentation generation
  • Managing larger codebases and longer context windows

Exercises​

Architecture Design Session​

Start by planning your feature:

  1. Identify a feature epic to implement
  2. Outline the high-level design (components, interfaces, data flow)
  3. Consider system impacts and constraints
  4. Break it down into smaller specs/user stories

Spec-Driven Expansion​

For each story in your epic:

  1. Write the specification (SDD Step 1)
  2. Generate task breakdown (SDD Step 2)
  3. Have AI implement (SDD Step 3)
  4. Validate and review (SDD Step 4)

You're now running this process with minimal guidance — you drive the decisions.

Check /insights as you go

Your usage patterns will shift as you move from bug fixes to feature design. Run /insights periodically during this module to catch new opportunities to improve your workflow.

System Integration​

Once features are built:

  • Test the end-to-end system
  • Verify components work together
  • Use AI to write integration tests
  • Debug any integration issues with AI assistance

Demo Your Feature​

Record a demo (5-10 minutes) showing:

  • The feature you built
  • How it integrates with the existing system
  • Your architecture decisions and why you made them
  • How AI helped you build it

Deliverables​

By the end of the Features & Systems Module, you must produce:

ArtifactDescription
Spec docsSpecifications for all stories in your epic
Task breakdownsHow you decomposed each story
PR linksAll merged PRs with AI code review
Test outputIncluding integration tests
Architecture docSystem design documentation (AI-assisted)
Demo recordingWalkthrough of your feature and approach
Short retroWhat changed in your workflow — focus on systems thinking

Exit Criteria​

Features & Systems Module Complete

You can move to the DevOps & Platform Module when you have:

Integrated a major AI feature across components/services.

Your feature should be working, tested, documented, and demonstrated. You should be able to explain your architecture decisions and how the components interact.

What You'll Learn​

By completing this module, you'll have experienced:

  • Rapidly adding complex capabilities with minimal friction
  • Systems thinking — how AI and software components interplay
  • Designing and building multi-component systems with AI
  • Making architecture decisions and defending them

You'll see that even major features can be done quickly with AI's help.