AI-Enabled Projects

Every Project Comes Pre-Configured for AI Assistance

Every project created by GenAI-Logic includes comprehensive training materials, readme's with code examples, and integration points that work seamlessly with GitHub Copilot, Claude, ChatGPT, and other AI assistants.

Your project includes .github/.copilot-instructions.md, AI training documents (docs/training), and working code examples that serve as a "message in a bottle" for AI assistants.

No more explaining your project structure from scratch - AI assistants can immediately understand your codebase and underlying software to help you build features.

AI assistants can help you add business logic rules, customize APIs, create test scenarios, and integrate with external services.

They also provide an AI Guided Tour, where an intelligent AI Assistant introduces you to the key concepts of GenAI-Logic.

 

This page describes:

• what makes your project AI-enabled out of the box
• the training materials included as your "message in a bottle"
• how to get started with AI assistance
• the AI-friendly workflows built into every project

 

AI Role: the 3-Legged Stool

GenAI-Logic provides functionality by a combination of core services (project creation, api execution, rules engine), and by leveraging/extending AI Assistants in your IDE.

Diagram - Tech Details

As shown above, GenAI-Logic functionality is delivered by 3 key elements:

1. Architecture Automation

GenAI-Logic provides automation both at Project Creation, and Runtime:

• Project Creation - schema discovery to create projects, with architecture automation to integrate and start the engines

• Runtime Engines - engines to execute APIs, Logic, Security, database access, etc

2. AI

The primary use of AI is to use your AI Assistant for:

• Authoring (e.g., create logic, APIs), and
• Explanations - find how the system works (e.g., how does logic work, what about performance, etc)

Importantly, these authoring services preserve Human in the Loop: review what AI creates, accept/alter as required. The resultant system is deterministic.

You can also elect to use AI at runtime, by specifying rules with Use AI to.... For example, the MCP AI Demo illustrates using AI to choose an optimal supplier - for more information, see MCP AI Example.

AI can be used to compute values, and we know AI can make mistakes.
Govern such AI Logic using business rules -- AI can propose, deterministic rules decide what commits.

We use the following models:

• CLI services use ChatGPT. You will need to configure your key, typically as an environment variable.

• Copilot access is your choice. We get good results and typically use Claude Sonnet 4.6.

3. Context Engineering

Each project contains thousands of lines of Context Engineering that inform AI Assistant about the CLI and runtime engines.

Leg	What it provides	Without it
Logic Automation (Rules, API Engines)	Correct, auto-enforced business logic across all write paths; enterprise API; governed AI execution	- Procedural Logic: Dependency bugs, hard to maintain - Fat API: Unshared, Path-dependent logic - Demo-class APIs (no optimistic locking, etc)
Generative AI	Rapid creation, iteration, test generation from natural language	Weeks of manual development
Context Engineering	Guides AI to the right architecture (declarative rules, proper data model)	AI defaults to "Fat API" procedural code — works but ungoverned

Key insight: Without Context Engineering, AI generates working demos that lack enterprise architecture. Without rules automation, AI generates procedural code with correctness bugs. Together: a several-week effort became 30 minutes, producing a correct, enterprise-class, fully tested system.

"A/B result: 16 declarative rules vs. equivalent procedural code with 2 critical bugs."

 

Moving the Starting Line

Traditional projects spend their first sprints on infrastructure — project scaffolding, database connectivity, ORM models, REST API plumbing (pagination, optimistic locking), admin UI, authentication, CI/CD. The actual business problem doesn't appear until the team is already depleted.

genai-logic create delivers all of that in seconds: a running multi-table API with pagination and optimistic locking, admin app, logic engine, auth skeleton, and CI/CD templates — committed and functional on day one. Your first sprint begins at the business problem.

Combined with Context Engineering, this means the declarative rules that encode your domain logic are the first meaningful code written — not the reward for surviving infrastructure month.

 

AI-Enabled Projects

AI is enabled as described below.

 

🤖 Context Engineering

When you create a new project with genai-logic create, project contains extensive Context Engineering to guide your AI Assistant to leverage Logic Automation.

Bootstrap this by telling your AI Assistant to bootstrap itself

Please load `.github/.copilot-instructions.md`

Your project includes comprehensive training materials that serve as a "message in a bottle" for AI assistants:

1. .github/.copilot-instructions.md - this is the "message in a bottle" that enabled your AI Assitant to understand GenAI-Logic projects, and deliver the services above
2. docs/training/ - AI training documents with detail examples and patterns
3. readme.md - Project overview with quick start instructions
4. Code examples - real working examples in the readme's throughout the project

Extend Context Engineering

You can cause VSCode Copilot to pre-load your own instructions by placing *.instructions.md files in .github. See the example in the Manager: samples/readme_samples.md:

 

🧠 Context-Aware Architecture

The project structure itself provides rich context for AI understanding:

• Declarative logic patterns in logic/declare_logic.py
• API endpoint examples with SQLAlchemy models
• Test scenarios that demonstrate business requirements
• Integration templates for common patterns

 

💡 AI-Friendly Workflows

Your project supports natural AI-assisted development:

• Natural language to business rules - Describe requirements, get executable logic
• Automated testing - Behave scenarios that serve as living documentation
• Code completion - Rich type hints and patterns for IDE assistance
• Documentation generation - Self-documenting APIs and logic

 

Training

There are important resources to help you get started.

 

🎓 AI Guided Tour

It's been clear for quite some time that lab-based training was far superior to death by powerpoint. But running labs is not simple - it usually requires in-person expertise to deal with inevitable problems.

AI enables us to put a "message in a bottle" - an AI tutor that can walk you through the tutorial, and, unlike a readme, support you:

• answer questions ("how do I customize this")
• get you unstuck ("ah, you forgot to start the server")

Key aspects of the tour:

• Provocation-based learning (not instruction)
• Hands-on discovery (doing, not reading)
• AI as companion during the lab (not before/after)

The tour begins in the manager, which encourages you to create the basic demo. That creates the basic_demo project, which provides a special readme to start the tour:

 

Ed: this was an interesting technical problem - AI prefers to be reactive (not driving a tutorial), and make decisions about 'that seems to be working'. We needed it to be proactive and not skip steps - to act outside its comfort zone. To read more, click here.

 

🚀 Quick Reference readmes

Each project includes working examples (see various readme files within the project) you can build upon:

• Pre-configured rules demonstrating common business patterns
• Sample API calls with proper request/response formats
• Test data and scenarios for immediate experimentation
• Integration hooks for external services

 

🎯 Next Steps

To find more:

• Logic Guide - Learn about declarative business rules
• API Documentation - Understand your auto-generated API
• Testing Guide - Write and run business scenarios
• Sample Projects - Explore working examples

Your AI-enabled project is ready to evolve with your needs. Just describe what you want, and let AI help you build it!