feat: add review_design pattern

[xvnpw]

What this Pull Request (PR) does

Adds pattern for review_design - pattern creates comprehensive review of provided design

Related issues

Screenshots

Input

# Architecture

This document outlines the architecture of the AI Nutrition-Pro application

## Containers Context diagram

mermaid
C4Container
    title Container diagram for AI Nutrition-Pro

    Container_Boundary(c0, "AI Nutrition-Pro") {
        Container(api_gateway, "API Gateway", "Kong", "Authentication of clients, filtering of input, rate limiting")
        Container(app_control_plane, "Web Control Plane", "Golang, AWS Elastic Container Service", "Provides control plane to onboard and manage clients, configuration and check billing data")
        ContainerDb(control_plan_db, "Control Plane Database", "Amazon RDS", "Stores all data related to control plan, tenants, billing")
        Container(backend_api, "API Application", "Golang, AWS Elastic Container Service", "Provides AI Nutrition-Pro functionality via API")
        ContainerDb(api_db, "API database", "Amazon RDS", "Stores dietitian' content samples, request and responses to LLM.")
        Person(admin, "Administrator", "Administrator of AI Nutrition-Pro application")
    }

    System_Ext(mealApp, "Meal Planner", "Application to create diets by dietitians")

    System_Ext(chatgpt, "ChatGPT-3.5", "LLM")

    Rel(mealApp, api_gateway, "Uses for AI content generation", "HTTPS/REST")
    Rel(api_gateway, backend_api, "Uses for AI content generation", "HTTPS/REST")
    Rel(admin, app_control_plane, "Configure system properties")
    Rel(backend_api, chatgpt, "Utilizes ChatGPT for LLM-featured content creation", "HTTPS/REST")

    Rel(app_control_plane, control_plan_db, "read/write data", "TLS")
    Rel(backend_api, api_db, "read/write data", "TLS")

### External systems and persons

| Name | Type | Description | Responsibilities |
| --- | --- | --- | --- |
| Meal Planner application | External system, web application | One of many Meal Planner applications that can be integrated with AI Nutrition-Pro. It connects to AI Nutrition-Pro using REST and HTTPS. | - uploads samples of dietitians' content to AI Nutrition-Pro <br/> - fetches AI generated results, e.g. diet introduction, from AI Nutrition-Pro | 
|  ChatGPT-3.5 | External system, API | It's OpenAI product, an LLM solution | It will be used to generate content based on provided samples. |
 
### AI Nutrition-Pro container context systems and persons

| Name | Type | Description | Responsibilities |
| --- | --- | --- | --- |
| Web Control Plane | Internal system, Web application | It's written using Golang and deployed as Docker container into AWS Elastic Container Service. It uses Control Plane Database to store data. It's used in 3 roles: Administrator, App Onboarding Manager, and Meal Planner application manager. | Provide control plane to onboard and manage clients, configuration and check billing data |
| Control Plane Database | Internal database, Amazon RDS instance | Database | storing data for Web Control Plane |
| API Gateway | Internal system, API Gateway | Kong API Gateway | - authentication <br> - rate limiting <br> - filtering of input |
| API Application | Internal system, API application | It's written using Golang and deployed as Docker container into AWS Elastic Container Service | Provides AI Nutrition-Pro functionality via API. |
| API database | Internal database, Amazon RDS instance | Stored data: samples of dietitians' content, requests, and responses to LLM. | Storing data for API Application |
| Administrator | Internal Person | Administrator of AI Nutrition-Pro application. | - manage server configuration <br> - resolve problems <br> |

### Security

1. Authentication with Meal Planner applications - each has individual API key.
2. Authorization of Meal Planner applications - API Gateway has ACL rules that allow or deny certain actions.
3. Encrypted network traffic - network traffic between Meal Planner applications and API Gateway is encrypted using TLS.

Output

# Architecture Review Analysis

## Architecture Clarity and Component Design

Good aspects:
- Clear separation of control plane and data plane
- Well-defined container boundaries
- Logical component separation with distinct responsibilities
- Use of managed services (AWS RDS, ECS) reduces operational complexity

Areas for improvement:
- Missing health check mechanisms between components
- No clear indication of monitoring or logging architecture
- Interface contracts between components not specified
- No indication of API versioning strategy

## External System Integrations

Good aspects:
- Simple REST/HTTPS integration pattern
- API Gateway provides consistent entry point
- Clear separation from external meal planner apps

Areas for improvement:
- Lack of circuit breakers for external system calls
- No retry policies defined for ChatGPT integration
- Missing API documentation standards
- No clear failover strategy for external system outages

## Security Architecture

Good aspects:
- TLS encryption for all communications
- API key authentication implementation
- ACL-based authorization
- Network traffic encryption

Areas for improvement:
- No mention of secrets management
- Missing API request/response validation
- Lack of rate limiting details per client
- No security scanning approach defined
- Missing audit logging architecture

## Performance, Scalability and Resilience

Good aspects:
- Containerized deployment enables horizontal scaling
- Separate databases for control and data planes
- Use of API Gateway for traffic management

Areas for improvement:
- No caching strategy defined
- Missing performance SLAs and metrics
- No clear database scaling approach
- Lack of disaster recovery planning
- No mention of backup strategies

## Data Management and Storage Security

Good aspects:
- Separate databases for different concerns
- Use of managed RDS service

Areas for improvement:
- No data retention policies defined
- Missing data classification scheme
- Unclear backup and restore procedures
- No mention of data encryption at rest
- Missing data archival strategy

## Maintainability, Flexibility and Future Growth

Good aspects:
- Containerized architecture enables easy updates
- Modular design with clear boundaries
- Use of modern cloud services

Areas for improvement:
- No CI/CD pipeline defined
- Missing feature flag strategy
- No clear approach for configuration management
- Lack of development environment strategy
- No mention of testing approach

## Potential Risks and Areas for Improvement

Key risks:
- Single point of failure at API Gateway
- Dependency on ChatGPT availability
- No clear data privacy controls
- Missing monitoring and alerting
- Undefined incident response process

Recommendations:
- Implement service mesh for resilience
- Add caching layer for performance
- Develop comprehensive monitoring
- Create detailed backup procedures
- Implement secrets management
- Define API versioning strategy

## Document Readability

Improvements needed:
- Add glossary of terms
- Include deployment diagram
- Add sequence diagrams for key flows
- Expand security section
- Include error handling scenarios
- Add performance requirements
- Document API contracts

## Summary

The architecture provides a solid foundation with good separation of concerns and use of modern cloud services. Critical improvements should focus on:

1. Resilience and fault tolerance
2. Monitoring and observability
3. Security hardening
4. Data management policies
5. Operational procedures
6. Documentation completeness

These improvements would significantly enhance the robustness and maintainability of the system.