The Architectural Possibilities Pattern leverages LLMs to generate and iteratively refine multiple software architectures for a given system and constraints, facilitating broader exploration and rapid experimentation.
This summary is mostly generated using Google Gemini from an academic paper (see Source below). The examples at the end are from actual interactions with ChatGPT (GPT-4o) and Google Gemini (2.5 Flash), modified slightly for clarity and brevity.
Architectural Possibilities Pattern
Intent and Context
- Generates diverse architectures: This pattern helps developers explore various software architectures with minimal effort.
- Flexible definition of “architecture”: The developer defines what “architecture” means to the Large Language Model (LLM), which can include file layout, module communication, system tiers, etc.
- Facilitates exploration: The primary goal is to allow developers to explore any architectural aspect of a system using an LLM.
- Interactive refinement: Developers can refine suggestions by adding constraints or asking the LLM to describe architectures in terms of different aspects (e.g., file layout, modules, services, communication patterns, infrastructure).
Motivation
- Reduces cognitive effort: Devising software architectures is challenging and time-consuming, often leading developers to consider only a few options.
- Expands architectural familiarity: Developers may not be aware of all suitable architectures; this pattern helps them explore unfamiliar but potentially good fits.
- Facilitates broad exploration: Given the critical role of architecture, it’s important to easily explore many alternatives, including those developers might not know about.
Structure and Key Ideas
The fundamental contextual statements for the Architectural Possibilities Pattern are:
- “I am developing a software system with X for Y.”
- “The system must adhere to these constraints.”
- “Describe N possible architectures for this system.”
- “Describe the architecture in terms of Q.”
Example Implementation
- Practical application: An example involves asking for architectures for a Python web application using FastAPI, similar to Twitter.
- Specific prompt: “I am developing a Python web application using FastAPI that allows users to publish interesting ChatGPT prompts, similar to Twitter. Describe three possible architectures for this system. Describe the architecture with respect to modules and the functionality that each module contains.”
- Importance of “with respect to”: This phrase is crucial for guiding the LLM’s output and focusing the architectural description on specific aspects (e.g., modules, REST API, service interaction, data storage, deployment).
Consequences
- Performance optimization: Can be used to propose architectures for performance goals and generate code experiments to test them (e.g., monolithic vs. microservice cloud applications).
- Rapid experimentation: The pattern is powerful when combined with LLM-based rapid implementation and experimentation, allowing for testing under various workloads and comparative cost analysis.
- Integration with other patterns:
- API Generator and API Simulator patterns: The architecture can serve as the basis for API generation and subsequent simulation, providing insight into code realization.
- Change Request Simulator pattern: Can be used to assess the difficulty of future changes given a proposed architecture.
Example
Simple Example with ChatGPT
> User:
I am developing a Python web application using FastAPI that allows users to publish interesting ChatGPT prompts, similar to Twitter. Describe three possible architectures for this system. Describe the architecture with respect to modules and the functionality that each module contains.
> ChatGPT:
Here are three possible architectures for your FastAPI-based web application that allows users to publish and interact with ChatGPT prompts. Each architecture is progressively more scalable and modular, depending on your goals for growth, performance, and maintainability.
---
### Architecture 1: Monolithic (Single Service)
Best for: MVP, early-stage development, simple deployments.
#### Modules & Responsibilities:
1. API Module (FastAPI Routes)
* Handles HTTP requests (e.g., create prompt, like prompt, list prompts).
* Contains request validation with Pydantic.
* Maps requests to service/business logic.
2. Service Layer
* Business logic (e.g., filtering inappropriate content, rate limits).
* Authentication checks, permissions.
* Orchestrates calls to the database and background tasks.
3. Data Access Layer (DAL)
* SQLAlchemy models.
* CRUD operations.
* Connection handling with a database (e.g., PostgreSQL).
4. Authentication Module
* JWT-based auth or OAuth2.
* Signup/Login/Token refresh.
5. Background Tasks
* Using FastAPI background tasks or Celery.
* Tasks: content moderation, prompt analytics, email notifications.
6. Frontend (Optional or External)
* Could be decoupled (React/Vue app) or embedded via Jinja templates.
* Calls API endpoints.
---
### Architecture 2: Modular Microservices with API Gateway
Best for: Growing teams, decoupled services, easier scaling and maintenance.
#### Services & Modules:
1. API Gateway (FastAPI or NGINX + FastAPI)
* Routes traffic to services.
* Handles rate limiting, auth token validation.
2. Prompt Service
* Handles creation, listing, liking of prompts.
* Business logic and DB access for prompts.
* Exposes gRPC or REST API.
3. User Service
* Manages user accounts, profiles, and authentication.
* Handles JWT or OAuth2 issuance.
* Stores user metadata (e.g., number of prompts posted).
4. Moderation Service
* Processes prompt content asynchronously.
* Uses ML or rules to flag/remove inappropriate content.
* Exposed as a background worker (Celery + Redis/RabbitMQ).
5. Frontend (React or Vue SPA)
* Communicates with the API Gateway.
* Auth token storage and handling.
* Dynamic UI for prompt feed, submission, user profile.
6. Shared Modules/Services
* Database (PostgreSQL for structured data, Redis for caching).
* Object storage (e.g., for prompt screenshots or metadata).
* Message queue for async task communication (Redis/RabbitMQ).
---
### Architecture 3: Event-Driven with CQRS and Async Processing
Best for: High-scale, performance-focused apps with analytics or machine learning needs.
#### Components & Responsibilities:
1. Command API (FastAPI)
* Handles user actions (create prompt, like, comment).
* Sends commands to a queue instead of writing directly to the DB.
* Authenticated endpoints.
2. Event Processor / Worker
* Listens to command events (e.g., `CreatePrompt`, `LikePrompt`).
* Validates, applies logic, writes to the write database.
* Publishes events (e.g., `PromptCreated`) to event bus (e.g., Kafka).
3. Query API (FastAPI)
* Read-optimized service.
* Serves the frontend with a read database (denormalized, optimized for queries).
* Supports fast feed generation, search, etc.
4. Projection Service
* Syncs events from the event bus to update the read DB.
* Handles transformations and indexing (e.g., for search).
5. Moderation and ML Services
* Independent services that subscribe to `PromptCreated` events.
* Analyze content, update metadata, or trigger moderation flows.
6. Frontend (SPA or Mobile App)
* Communicates with the query API for reading data.
* Sends actions to the command API.
7. Infrastructure
* Kafka/Redis Streams for event bus.
* PostgreSQL (write), ElasticSearch/Redis (read).
* Object Storage (e.g., S3) for metadata, snapshots.
---
### Summary Comparison
| Feature | Monolithic | Modular Microservices | Event-Driven |
| ----------- | ----------------- | --------------------- | ------------------------- |
| Complexity | Low | Medium | High |
| Deployment | Single unit | Multiple containers | Event infrastructure |
| Scalability | Limited | Service-level | Fine-grained |
| Ideal Use | MVP, early launch | Growing app | High scale, analytics, ML |
A More Realistic Example with ChatGPT
> User:
I am developing a web application that allows users to publish interesting LLM prompts, similar to Twitter. The important aspects of this application are as follows:
* Users need to register with and log in to the application before they can post content.
* Administrators need to be able to perform user and content management.
* There is an automated content moderation system to prevent malicious posts.
* There is an automated example generation system to show example LLM outputs for the prompts.
* The application is cloud-native, mainly deployed on AWS.
* The application can use other external services for functionalities that are inadequately provided within AWS.
* The application can accept and publish 100s of posts per second.
* The application can publish each post, without an example LLM output, within 5 seconds of posting.
* The application can add an example LLM output to a published post within 10 seconds after the post has been published.
* The application must be able to scale down/in its resource usage at times of low traffic to reduce running costs.
Describe 3 possible architectures for this system. Describe the architecture with respect to components/services and the functionality that each component/service provides.
> ChatGPT:
Here are three distinct cloud-native architectures for your web application, each optimized for your functional and non-functional requirements. All three ensure scalability, reliability, and responsiveness, but vary by complexity, cost-efficiency, and technology preferences.
---
## Architecture 1: AWS-Centric Microservices with Serverless Core
### Ideal for: Simplicity, cost-efficiency, and leveraging AWS native services with minimal ops overhead.
### Components & Functionality
| Component | AWS Service | Functionality |
| ------------------------- | ---------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------- |
| Frontend | Amazon CloudFront + S3 (static site) or Amplify | Serves the React/Vue web app over CDN. Fast and globally distributed. |
| API Gateway | Amazon API Gateway | Handles HTTP(S) routing and request authentication. |
| Auth | Amazon Cognito | Handles user registration, login, and token-based session management. |
| Backend APIs | AWS Lambda (Node.js/Python) | Stateless microservices handling prompt posting, fetching, moderation, etc. |
| Database | Amazon DynamoDB | Stores users, prompts, moderation status, LLM output metadata. High throughput with auto-scaling. |
| Content Moderation | Lambda + Amazon Comprehend / AWS Rekognition or third-party API (e.g. Hive) | Automatically scans submitted prompts for policy violations. |
| LLM Output Generator | Event-driven Lambda + Amazon SQS + External LLM API (e.g. OpenAI, Anthropic) | Generates and attaches example outputs asynchronously. |
| Messaging/Event Queue | Amazon SQS | Buffers prompt posts to decouple write-heavy traffic and trigger moderation + LLM processing. |
| Admin Interface | Amplify + Lambda | Internal dashboard for managing users, viewing flagged content, etc. |
| Monitoring & Auto-scaling | CloudWatch + Lambda concurrency + DynamoDB auto-scaling | Monitors performance and scales down during idle times. |
### Strengths
* Fully serverless → easy scaling and cost-efficiency.
* Uses native AWS autoscaling capabilities.
* Event-driven: moderation and LLM processing run in parallel.
### Trade-offs
* Slightly higher cold-start latency.
* Requires careful IAM and API Gateway setup.
---
## Architecture 2: Kubernetes-Based Microservices with External Integrations
### Ideal for: Flexibility, custom workflows, high concurrency workloads.
### Components & Functionality
| Component | Service | Functionality |
| -------------------- | ---------------------------------------------------- | --------------------------------------------------------------------- |
| Frontend | NGINX + React/Vue hosted on EKS | Web UI served from pods behind ingress controller. |
| API Layer | Node.js/Go REST APIs in EKS pods | Handles user interaction, post creation, content retrieval. |
| Auth | Amazon Cognito or Keycloak in EKS | AuthN/Z for users and admins. |
| Database | Amazon Aurora (PostgreSQL) or MongoDB Atlas | Stores user data, prompt content, moderation status, output examples. |
| Content Moderation | Python microservice + External API (Hive/Modzy/etc.) | Processes new prompts for violations. |
| LLM Output Generator | Python/Go worker pods in K8s, triggered via Kafka | Calls OpenAI or other LLM APIs to generate examples. |
| Async Messaging | Apache Kafka or Amazon MSK | Buffers post events, triggers moderation and LLM services. |
| Admin Dashboard | Internal service in EKS with RBAC | Admins manage users, flagged posts, moderation queue. |
| Monitoring | Prometheus + Grafana + K8s HPA | Live monitoring and horizontal scaling based on CPU or queue depth. |
### Strengths
* Full control over orchestration and autoscaling logic.
* Easier to run hybrid workloads or experiment with new ML services.
* Better fit for heavy/modular custom logic.
### Trade-offs
* Requires more DevOps resources.
* More complex to operate than a serverless model.
---
## Architecture 3: Hybrid Serverless + Event Mesh + LLM Fine-tuning
### Ideal for: High-concurrency LLM workflows, multi-cloud integrations, and performance guarantees.
### Components & Functionality
| Component | Service | Functionality |
| -------------------- | ------------------------------------------------- | --------------------------------------------------------------------- |
| Frontend | Next.js on Vercel or CloudFront + S3 | Interactive app with SSR for SEO. |
| Edge Authentication | Auth0 or Firebase Auth | OAuth2, MFA, user profile management. |
| API Layer | AWS App Runner + FastAPI/Golang | Auto-scaling containers to handle requests faster than Lambda. |
| Database | Amazon Aurora Serverless (v2) | Highly scalable relational DB for user/prompt storage. |
| Event Bus | Amazon EventBridge or Temporal.io | Orchestrates prompt lifecycle events: post → moderate → LLM → notify. |
| Content Moderation | AWS Comprehend + Lambda + External APIs | Moderation pipelines triggered by EventBridge. |
| LLM Output Generator | ECS Fargate Tasks or SageMaker Inference + OpenAI | Batch example generation and post enrichment within 10s. |
| Admin Tools | Low-code (Retool, Appsmith) | Admin tools integrated with APIs and DB. |
| Observability | AWS X-Ray + CloudWatch + Sentry | End-to-end tracing and alerting. |
| Cost Optimizer | Scheduled ECS/Lambda scaling policies | Scheduled or usage-based scale-in at night/low traffic. |
### Strengths
* Balanced use of containers and serverless.
* Temporal/EventBridge makes LLM workflows easier to manage and retry.
* High performance and flexible for complex data flows.
### Trade-offs
* Requires knowledge of event-driven design and state machines.
* Slightly more expensive than pure serverless during low-load times.
---
## Comparison Summary
| Feature | Architecture 1 (Serverless) | Architecture 2 (Kubernetes) | Architecture 3 (Hybrid + Event-Driven) |
| ------------------------ | --------------------------- | --------------------------- | -------------------------------------- |
| Simplicity | ✅ High | ❌ Medium-Low | ⚠️ Medium |
| Scaling | ✅ Auto w/ Lambda/DynamoDB | ✅ HPA/Custom | ✅ Event-driven/Elastic |
| Latency Control | ⚠️ Cold-start concern | ✅ Predictable | ✅ Predictable |
| LLM Workflow Flexibility | ⚠️ Basic | ✅ Advanced | ✅ Very High |
| Ops Overhead | ✅ Low | ❌ High | ⚠️ Medium |
| Cost-Efficiency | ✅ Very High | ⚠️ Variable | ✅ Good (with tuning) |
| Dev Speed | ✅ Fast | ⚠️ Slower | ✅ Moderate |
---
Would you like a visual architecture diagram or Terraform templates for one of these?
MVP Architectures from the Realistic ChatGPT Example
> User:
Referring to Architecture 3 (Hybrid Serverless + Event Mesh + LLM Fine-tuning), describe 2 possible MVP architectures. The important aspects of the MVP architectures are as follows:
* Same requirements as before, except you can ignore all performance-related requirements.
* Optimise the architecture for simplicity, speed of development, and running cost.
* The architecture must allow incremental changes to gradually evolve it towards Architecture 3.
Describe each architecture with respect to components/services and the functionality that each component/service provides, along with its strengths and trade-offs. At the end, provide a table summarising the comparison between the architectures.
> ChatGPT:
Great — focusing on MVP architectures that are simple, low-cost, and fast to build, while keeping the door open for gradual evolution toward the more sophisticated Hybrid Serverless + Event Mesh architecture (Architecture 3), here are two possible MVP architectures.
---
## MVP Architecture 1: Fully Serverless with Minimal AWS Services
### Components & Functionality
| Component | Service | Functionality |
| --------------------- | ------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------- |
| Frontend | AWS Amplify Hosting or S3 + CloudFront | Hosts static frontend (React/Next.js). CDN for global access. |
| Authentication | AWS Cognito | Handles user sign-up, login, and JWT-based session auth. |
| API Layer | AWS API Gateway + AWS Lambda (Python/Node.js) | Handles RESTful endpoints for prompt posting, user actions, etc. |
| Database | Amazon DynamoDB | Stores users, posts, moderation status, and generated outputs. |
| Content Moderation | Lambda function with simple rules or integrated with a 3rd-party API (e.g., Hive, OpenAI moderation endpoint) | Moderates prompt content before publishing. |
| LLM Example Generator | Lambda function + direct call to OpenAI API | Triggered asynchronously (via SQS or Step Functions) to enrich prompt posts. |
| Async Workflow | Amazon SQS | Stores post events and triggers LLM generation Lambda. |
| Admin Interface | Internal frontend page | Displays flagged posts, user actions via protected APIs. |
### Strengths
* Extremely low operational overhead.
* Easy to deploy and iterate.
* Mostly pay-per-use = low running cost at low scale.
* Fastest path to a usable prototype.
### Trade-offs
* Harder to manage complex workflows as the system grows.
* DynamoDB limitations if advanced querying is needed later.
* No central event orchestrator (EventBridge or Temporal), which may complicate retries and long-running logic later.
---
## MVP Architecture 2: Event-Driven Core with Lightweight Containers
### Components & Functionality
| Component | Service | Functionality |
| --------------------- | ------------------------------------------------------ | ----------------------------------------------------------------------- |
| Frontend | Vercel (Next.js) or S3 + CloudFront | Server-rendered or static site for faster iteration. |
| Authentication | Auth0 (or Firebase Auth) | Fast integration for user registration/login. |
| API Layer | AWS App Runner (FastAPI/Golang container) | REST endpoints handling prompt creation, retrieval, etc. |
| Database | Amazon Aurora Serverless v2 (PostgreSQL) | Relational DB with auto-scaling. Ideal for future growth. |
| Content Moderation | Lambda or container calling OpenAI moderation endpoint | Moderates prompt before publishing. |
| Async Messaging | Amazon EventBridge | Captures post creation events to trigger moderation and LLM generation. |
| LLM Example Generator | Lambda (initially) or App Runner job | Asynchronously enriches prompts using external LLM API. |
| Admin Interface | Admin UI served via Vercel or embedded in frontend | Protected dashboard for managing content and users. |
### Strengths
* More closely resembles final architecture (event mesh + containers).
* PostgreSQL schema allows complex queries, joins, and future analytics.
* EventBridge enables easier transition to retries, monitoring, and workflow chaining.
* Smooth transition path to Fargate/ECS or Temporal later.
### Trade-offs
* Slightly more complex to deploy than MVP 1 (requires container build/deploy).
* Costs can be higher (App Runner + Aurora) if not tuned carefully.
* EventBridge has a learning curve compared to simpler SQS flows.
---
## Comparison Table: MVP 1 vs. MVP 2
| Feature | MVP 1: Fully Serverless | MVP 2: Event-Driven with Containers |
| -------------------------- | -------------------------------------------- | ------------------------------------------------- |
| Dev Speed | ✅ Fastest (mostly AWS-managed) | ⚠️ Slightly slower (containers, custom auth) |
| Ops Overhead | ✅ Very Low | ⚠️ Moderate (App Runner + EventBridge) |
| Cost at Low Scale | ✅ Very Low (pay-per-use) | ⚠️ Slightly Higher (Aurora, App Runner base cost) |
| Moderation & LLM Flow | Basic, uses SQS + Lambda | EventBridge + richer orchestration |
| Data Storage | DynamoDB (simple, scalable) | Aurora Serverless (flexible, query-rich) |
| Frontend Hosting | Amplify or S3 | Vercel or S3 |
| Auth | Cognito | Auth0 or Firebase Auth |
| Scalability Path | Easy migration to Step Functions/EventBridge | Smooth transition to full hybrid/EventMesh |
| Evolvability to Final Arch | ⚠️ Requires rethinking state mgmt | ✅ Close to Architecture 3 |
---
## Recommendation
* Start with MVP 1 if:
* You want to prototype rapidly, validate UX and core flows, and pay nearly nothing when idle.
* You’re more familiar with Lambda and DynamoDB.
* Start with MVP 2 if:
* You want to lay the groundwork for an event-driven architecture and use PostgreSQL for rich querying.
* You’re comfortable with containers and event routing, and okay with slightly more infra overhead.
Would you like a visual diagram or Terraform/CDK boilerplate for either of these MVPs?
A More Realistic Example with Google Gemini
> User:
I am developing a web application that allows users to publish interesting LLM prompts, similar to Twitter. The important aspects of this application are as follows:
* Users need to register with and log in to the application before they can post content.
* Administrators need to be able to perform user and content management.
* There is an automated content moderation system to prevent malicious posts.
* There is an automated example generation system to show example LLM outputs for the prompts.
* The application is cloud-native, mainly deployed on AWS.
* The application can use other external services for functionalities that are inadequately provided within AWS.
* The application can accept and publish 100s of posts per second.
* The application can publish each post, without an example LLM output, within 5 seconds of posting.
* The application can add an example LLM output to a published post within 10 seconds after the post has been published.
* The application must be able to scale down/in its resource usage at times of low traffic to reduce running costs.
Describe 3 possible architectures for this system. Describe the architecture with respect to components/services and the functionality that each component/service provides.
> Gemini:
* User Management (Authentication & Authorization):
* AWS Cognito: Handles user registration, login, and identity management. It provides user directories, authentication flows (MFA, social logins), and issues JWT tokens for authorization.
* API Gateway:
* Amazon API Gateway (REST API or HTTP API): Acts as the entry point for all client requests (posting prompts, viewing prompts, user interactions). It handles API routing, request validation, throttling, and integrates directly with Lambda functions.
* Core Application Logic (Posting, Viewing, Moderation Trigger):
* AWS Lambda: Serverless compute for all business logic.
* `postPromptLambda`: Triggered by API Gateway for new prompt submissions. Validates input, stores the prompt, and publishes an event for moderation and example generation.
* `getPromptLambda`: Triggered by API Gateway to retrieve prompts. Queries the database.
* `userManagementLambda`: Integrates with Cognito for administrative user actions (e.g., password resets).
* Data Storage:
* Amazon DynamoDB: NoSQL database for storing prompt data (prompt text, user ID, timestamp, moderation status, example output reference). Its on-demand capacity mode and auto-scaling are well-suited for high write/read throughput.
* Content Moderation:
* Amazon EventBridge / SQS:
* `moderationTopic` (EventBridge or SQS Queue): `postPromptLambda` publishes a "New Prompt" event here.
* AWS Lambda (`moderatePromptLambda`): Subscribes to `moderationTopic`.
* External Content Moderation API (e.g., Azure Content Moderator, Google Cloud Content Safety, or a specialized SaaS): `moderatePromptLambda` calls this external API to analyze prompt content for maliciousness.
* Machine Learning Service (Optional - within AWS): If building custom moderation, Amazon Rekognition (for text analysis if applicable to LLM outputs) or Amazon Comprehend could be used within `moderatePromptLambda`.
* Example Generation:
* Amazon SQS (`exampleGenerationQueue`): `postPromptLambda` publishes a message to this queue containing the prompt ID and text. This decouples the example generation process from the initial post.
* AWS Lambda (`generateExampleLambda`): Subscribes to `exampleGenerationQueue`.
* External LLM API (e.g., OpenAI API, Anthropic Claude, Google Gemini API): `generateExampleLambda` calls this external service with the prompt to generate an example output.
* Amazon Bedrock (if using AWS's LLM offerings): If Bedrock offers suitable LLMs, this would be an AWS-native option.
* AWS Lambda (`updatePromptWithExampleLambda`): Triggered by `generateExampleLambda` after the example is generated to update the DynamoDB entry for the prompt.
* Asynchronous Processing & Eventing:
* Amazon SQS (Simple Queue Service): Used for reliable, asynchronous communication between services (e.g., new prompt to moderation/example generation).
* Amazon EventBridge: Event bus for routing events between services, enabling flexible event-driven architectures.
* Content Delivery:
* Amazon CloudFront: Content Delivery Network (CDN) to cache static assets (frontend application files) and potentially API responses, improving performance and reducing load on the origin.
* Amazon S3: Stores static frontend assets (HTML, CSS, JavaScript).
* Administration/Management:
* AWS Console: For general AWS resource management.
* AWS Amplify Admin UI (Optional): Can provide a quick way to build an admin interface for data management if using Amplify.
* Custom Admin UI (React/Vue/Angular deployed on S3/CloudFront) backed by API Gateway/Lambda: For more specific management tasks (e.g., user banning, content review).
Flows:
1. User Posts Prompt: User authenticates via Cognito -> API Gateway -> `postPromptLambda`.
2. Initial Post (within 5s): `postPromptLambda` stores prompt in DynamoDB (initial status "pending moderation", no example) -> returns success to user.
3. Asynchronous Moderation: `postPromptLambda` sends message to `moderationTopic` (EventBridge/SQS) -> `moderatePromptLambda` consumes message -> calls external moderation API -> updates prompt status in DynamoDB.
4. Asynchronous Example Generation: `postPromptLambda` sends message to `exampleGenerationQueue` (SQS) -> `generateExampleLambda` consumes message -> calls external LLM API -> `updatePromptWithExampleLambda` updates DynamoDB with example output.
5. User Views Prompt: API Gateway -> `getPromptLambda` -> DynamoDB.
Advantages:
* High Scalability: Lambda, API Gateway, and DynamoDB scale automatically and massively.
* Cost-Effective: Pay-per-execution model for Lambda and API Gateway, and on-demand for DynamoDB, making it cost-efficient during low traffic.
* Rapid Development: Focus on business logic with less infrastructure management.
* Resilience: Decoupled services reduce single points of failure.
Disadvantages:
* Cold Starts: Lambda functions can experience "cold starts" which might introduce slight latency for infrequent invocations (mitigated by provisioned concurrency if needed).
* Complexity of Distributed Systems: Debugging across multiple microservices can be challenging.
* Vendor Lock-in: Strong reliance on AWS services.
---
## Architecture 2: Containerized Microservices with Managed Services
This architecture uses containers for more control over the application environment and leverages managed container orchestration, balancing flexibility with operational overhead.
Components and Functionality:
* User Management (Authentication & Authorization):
* AWS Cognito: Same as Architecture 1.
* Load Balancing & API Entry Point:
* Application Load Balancer (ALB): Distributes incoming traffic to the containerized services. Provides SSL termination, request routing, and basic load balancing.
* Container Orchestration:
* Amazon ECS (Elastic Container Service) with Fargate Launch Type: Runs your microservices as containers without managing underlying EC2 instances. Fargate handles server provisioning, patching, and scaling.
* `AuthService`: Handles user authentication and authorization logic, interacting with Cognito.
* `PromptService`: Manages CRUD operations for prompts, interacts with the database.
* `ModerationTriggerService`: Publishes messages to the moderation queue when a new prompt is posted.
* `ExampleGenerationTriggerService`: Publishes messages to the example generation queue when a new prompt is posted.
* Alternatively, Amazon EKS (Elastic Kubernetes Service): For larger, more complex deployments requiring higher levels of orchestration and portability, EKS could be used, but adds significant operational complexity.
* Data Storage:
* Amazon Aurora (PostgreSQL or MySQL Compatible): Relational database for storing prompt data. Provides high availability, scalability (read replicas), and good performance for structured data.
* Amazon ElastiCache (Redis): Optional, for caching frequently accessed prompts or user data to reduce database load and improve read performance.
* Content Moderation:
* Amazon SQS (`moderationQueue`): Queue for new prompts awaiting moderation.
* ECS Service (`ModerationWorkerService`): Consumes messages from `moderationQueue`.
* External Content Moderation API: Calls an external service for moderation.
* Updates prompt status in Aurora.
* Example Generation:
* Amazon SQS (`exampleGenerationQueue`): Queue for prompts awaiting example generation.
* ECS Service (`ExampleGenerationWorkerService`): Consumes messages from `exampleGenerationQueue`.
* External LLM API (or Amazon Bedrock): Calls the LLM to generate output.
* Updates prompt with example in Aurora.
* Asynchronous Processing & Eventing:
* Amazon SQS: Essential for decoupling services and handling asynchronous workflows.
* Content Delivery:
* Amazon CloudFront & Amazon S3: Same as Architecture 1 for static frontend assets.
* Administration/Management:
* AWS Console: For general AWS resource management.
* Custom Admin UI (frontend deployed on S3/CloudFront) backed by `PromptService` and `AuthService` endpoints.
Flows:
1. User Posts Prompt: User authenticates (via Cognito/AuthService) -> ALB -> `PromptService`.
2. Initial Post (within 5s): `PromptService` stores prompt in Aurora -> publishes message to `moderationQueue` and `exampleGenerationQueue` -> returns success to user.
3. Asynchronous Moderation: `ModerationWorkerService` consumes from `moderationQueue` -> calls external moderation API -> updates Aurora.
4. Asynchronous Example Generation: `ExampleGenerationWorkerService` consumes from `exampleGenerationQueue` -> calls external LLM API -> updates Aurora.
5. User Views Prompt: ALB -> `PromptService` -> Aurora (potentially ElastiCache).
Advantages:
* More Control: Containers provide more control over the application environment and dependencies.
* Portability: Containerized applications are generally more portable across environments.
* Scalability: ECS Fargate scales containers automatically based on defined metrics.
* Cost Optimization: Fargate's pay-per-second billing for compute and memory helps optimize costs, and scaling down inactive services is straightforward.
Disadvantages:
* Higher Operational Overhead: Even with Fargate, there's more to manage than pure serverless (e.g., container images, service definitions).
* Potentially Higher Costs than Pure Serverless: While Fargate is cost-effective, running containers 24/7 for baseline traffic can be more expensive than Lambda's execution model.
* Slightly Slower Development Cycle: Building and deploying container images can add a small overhead compared to direct Lambda deployments.
---
## Architecture 3: Hybrid with Managed Services and Specialized External Platforms
This architecture leverages AWS for core infrastructure but explicitly integrates with best-of-breed external platforms for specific functionalities, especially for the LLM interaction.
Components and Functionality:
* User Management (Authentication & Authorization):
* AWS Cognito: Same as previous architectures.
* API Gateway:
* Amazon API Gateway (REST API): Entry point for all interactions.
* Core Application Logic:
* AWS Lambda: Used for lightweight API handlers and event processors.
* Amazon ECS on Fargate: For more complex or stateful microservices if needed, or for services that benefit from consistent warm instances.
* Data Storage:
* Amazon DynamoDB: For core prompt data, offering high throughput and low latency.
* Amazon S3: For storing larger prompt-related assets or potentially raw LLM outputs if they exceed DynamoDB limits or benefit from object storage.
* Content Moderation:
* Amazon SQS (`moderationQueue`): Queue for moderation tasks.
* AWS Lambda (`moderatePromptLambda`) or ECS Service (`ModerationWorker`): Consumes from the queue.
* Dedicated External Content Moderation Platform (e.g., Google Cloud Content Safety, Azure Content Moderator, or specialized SaaS like Sightengine, AI-based content moderation platforms): This platform is chosen for its superior capabilities and focus on content safety. The worker simply calls its API.
* Example Generation (The "Hybrid" Part):
* Amazon SQS (`exampleGenerationQueue`): Queue for example generation tasks.
* AWS Lambda (`generateExampleLambda`) or ECS Service (`ExampleGenerator`): Consumes from the queue.
* Dedicated External LLM Platform (e.g., OpenAI API, Anthropic Claude, Google Gemini API, or a specialized LLM inference provider): The application explicitly relies on a non-AWS LLM platform known for its performance, model variety, or specific features.
* Callback/Webhook to update AWS: The external LLM platform might send a webhook back to an API Gateway endpoint (which triggers a Lambda) or publish to an SQS queue once the example is ready, rather than waiting for the `generateExampleLambda` to poll.
* Asynchronous Processing & Eventing:
* Amazon SQS: Core for decoupling and reliability.
* Amazon EventBridge: For broader event distribution and integration with other services.
* Content Delivery:
* Amazon CloudFront & Amazon S3: For static frontend assets.
* Monitoring & Logging:
* Amazon CloudWatch: For logs, metrics, and alarms from AWS services.
* External APM Tool (e.g., Datadog, New Relic): For end-to-end tracing, performance monitoring across AWS and external services, and unified dashboards.
* CI/CD:
* AWS CodePipeline/CodeBuild/CodeDeploy: For automated deployments.
* GitHub Actions/GitLab CI/CD: As an alternative, integrating with AWS.
Flows:
1. User Posts Prompt: User authenticates -> API Gateway -> `postPromptLambda` (or ECS service).
2. Initial Post (within 5s): `postPromptLambda` stores prompt in DynamoDB -> sends message to `moderationQueue` and `exampleGenerationQueue`.
3. Asynchronous Moderation: `ModerationWorker` consumes -> calls external moderation platform -> updates DynamoDB.
4. Asynchronous Example Generation: `ExampleGenerator` consumes -> calls external LLM platform.
* Option A (Polling): `ExampleGenerator` polls the external LLM platform for completion, then updates DynamoDB.
* Option B (Webhook): External LLM platform sends a webhook to API Gateway endpoint (which triggers a Lambda) once complete, updating DynamoDB.
5. User Views Prompt: API Gateway -> `getPromptLambda` -> DynamoDB.
Advantages:
* Best-of-Breed Services: Leverages highly specialized and potentially more advanced external services for core functionalities like LLM inference and content moderation.
* Flexibility: Not entirely tied to AWS's specific offerings for every component.
* Potential Performance Gains: Dedicated LLM inference platforms might offer better performance or lower latency for specific models.
* Reduced Development Effort: Offloading complex tasks like LLM fine-tuning or sophisticated moderation rules to external platforms can save internal development time.
Disadvantages:
* Increased Integration Complexity: Managing integrations with multiple external APIs, including authentication, rate limits, and error handling.
* Dependency on External Vendors: Reliance on the uptime, performance, and pricing of third-party services.
* Cost Management: Need to track costs across AWS and external service providers.
* Security Considerations: Managing API keys and credentials for external services.
---
Common Architectural Considerations Across All Options:
* Security:
* IAM Roles and Policies: Least privilege access for all AWS services.
* VPC, Subnets, Security Groups, Network ACLs: Network isolation and security.
* AWS WAF (Web Application Firewall): Protects API Gateway and ALBs from common web exploits.
* AWS Secrets Manager: Securely store API keys for external services.
* Data Encryption: Encryption at rest (DynamoDB, Aurora, S3) and in transit (HTTPS/SSL).
* Monitoring & Logging:
* Amazon CloudWatch: Centralized logging, metrics, and alarms for all AWS services.
* AWS X-Ray: Distributed tracing for microservices architectures.
* CI/CD:
* AWS CodePipeline, CodeBuild, CodeDeploy: For automated build, test, and deployment.
* Infrastructure as Code (IaC): AWS CloudFormation or Terraform to define and manage infrastructure.
* Cost Optimization:
* Leverage serverless and managed services (Lambda, Fargate, DynamoDB) for automatic scaling and pay-per-use.
* Utilize auto-scaling groups for EC2-based services (if used).
* Implement efficient database indexing and query patterns.
* High Availability & Disaster Recovery:
* Deploy services across multiple Availability Zones (AZs).
* Utilize database replication (Aurora read replicas, DynamoDB global tables).
* Regular backups.
These three architectures provide distinct approaches, each with its own trade-offs, to building your LLM prompt sharing application on AWS. The best choice will depend on your specific team's expertise, performance requirements, budget, and desired level of control.
MVP Architectures from the Realistic Gemini Example
> User:
Referring to Architecture 3 (Hybrid with Managed Services and Specialized External Platforms), describe 2 possible MVP architectures. The important aspects of the MVP architectures are as follows:
* Same requirements as before, except you can ignore all performance-related requirements.
* Optimise the architecture for simplicity, speed of development, and running cost.
* The architecture must allow incremental changes to gradually evolve it towards Architecture 3.
Describe each architecture with respect to components/services and the functionality that each component/service provides, along with its strengths and trade-offs. At the end, provide a table summarising the comparison between the architectures.
> Gemini:
## MVP Architectures for a Hybrid LLM Prompt Application (Based on Architecture 3)
These MVP architectures prioritize simplicity, rapid development, and cost-efficiency while laying the groundwork for a gradual evolution towards the full-fledged Architecture 3. We'll simplify the asynchronous processing and focus on getting core functionalities working.
### MVP Architecture 1: Lambda-Centric with Direct Integrations
This MVP prioritizes serverless functions (AWS Lambda) for all core logic and directly integrates with external services. This minimizes infrastructure setup and operational overhead.
Components and Functionality:
* User Management (Authentication & Authorization):
* AWS Cognito: Handles user registration, login, and identity management. Provides a hosted UI for quick setup, and user pools for managing users.
* API Gateway:
* Amazon API Gateway (REST API): The single entry point for all client requests. Handles API routing and integrates directly with Lambda functions.
* Core Application Logic:
* AWS Lambda (`PromptHandlerLambda`): A single, monolithic (or near-monolithic for MVP) Lambda function that handles most prompt-related operations:
* Posting Prompts: Receives prompt data, performs basic validation, stores in DynamoDB, *and directly calls the external moderation API*.
* Viewing Prompts: Retrieves prompts from DynamoDB.
* Triggering Example Generation: *Directly calls the external LLM API to generate the example*, then updates the DynamoDB entry with the example. This is synchronous in the MVP for simplicity, which is acceptable given the relaxed performance requirements.
* AWS Lambda (`AuthLambda` - Optional/Integrated): For simplicity, authentication logic might be handled within `PromptHandlerLambda` by verifying Cognito JWTs, or a very thin separate Lambda for specific auth actions.
* Data Storage:
* Amazon DynamoDB: NoSQL database for storing prompt data. Its serverless nature, on-demand capacity, and auto-scaling make it ideal for MVP cost-efficiency and quick setup. Stores prompt text, user ID, moderation status, and the generated example LLM output.
* Content Moderation:
* External Content Moderation Platform API: `PromptHandlerLambda` makes a direct synchronous call to this external service (e.g., Google Cloud Content Safety, Azure Content Moderator) immediately after a post is received and before it's marked as fully "published" for viewing.
* Example Generation:
* External LLM Platform API: `PromptHandlerLambda` makes a direct synchronous call to this external service (e.g., OpenAI API, Anthropic Claude, Google Gemini API) to generate the LLM output.
* Content Delivery:
* Amazon S3: Stores static frontend assets (HTML, CSS, JavaScript).
* Amazon CloudFront: (Optional for MVP, but good for performance) CDN to serve S3 content, reducing latency for users and offloading S3. Could be added later if needed.
* Administration:
* AWS Console: For direct management of DynamoDB data and Lambda logs.
* (Minimal) Custom Admin UI: A simple HTML page hosted on S3, backed by specific `PromptHandlerLambda` endpoints, to view/edit/delete prompts if absolutely necessary for early testing.
Strengths:
* Maximum Simplicity: Very few AWS services to manage, minimal configuration.
* Fastest Development: Logic is largely contained within a few Lambda functions. Direct synchronous calls eliminate the complexity of queues and workers initially.
* Lowest Running Cost (Initial): Purely serverless, you only pay for what you use. No always-on instances.
* Clear Evolution Path: Asynchronous processing (SQS/EventBridge, dedicated workers) can be introduced later around the `PromptHandlerLambda` as performance requirements become critical.
* Evolution to Async Moderation/Example Generation:
* Change `PromptHandlerLambda` to publish to SQS queues for moderation and example generation.
* Create new `ModerationWorkerLambda` and `ExampleGenerationWorkerLambda` functions that consume from these queues.
* Introduce a separate `UpdatePromptLambda` to handle updates back to DynamoDB.
Trade-offs:
* Synchronous Operations: Moderation and example generation happen in the same request path as prompt submission. This means the user might wait longer for the initial "post successful" response if these external calls are slow. (Acceptable for relaxed performance MVP).
* Monolithic Lambda: As more logic is added, the `PromptHandlerLambda` can become large and harder to maintain.
* Limited Scalability for External Calls: While Lambda scales, the *rate limits* of external APIs could become a bottleneck if traffic suddenly spikes (less of a concern for MVP but important for future).
* Potential for Timeouts: If external API calls take longer than Lambda's timeout (max 15 mins), the invocation will fail. This is less likely with prompt moderation/generation but a design consideration.
---
### MVP Architecture 2: API-Driven with Dedicated Workers
This MVP introduces a clearer separation between the immediate user-facing API and asynchronous background processes, using queues for communication. It's slightly more complex than MVP 1 but provides a more direct path to the full Architecture 3's asynchronous nature.
Components and Functionality:
* User Management (Authentication & Authorization):
* AWS Cognito: Same as MVP 1.
* API Gateway:
* Amazon API Gateway (REST API): Entry point for all client requests.
* Core Application Logic (API Layer):
* AWS Lambda (`PostPromptApiLambda`): Handles prompt submission. Validates input, stores the initial prompt in DynamoDB, and *immediately publishes messages to SQS queues for moderation and example generation*. Returns success to the user within 5 seconds (as per eventual requirement).
* AWS Lambda (`GetPromptApiLambda`): Retrieves prompts from DynamoDB.
* Data Storage:
* Amazon DynamoDB: Same as MVP 1.
* Asynchronous Processing & Decoupling:
* Amazon SQS (`moderationQueue`): Standard queue for messages about new prompts requiring moderation.
* Amazon SQS (`exampleGenerationQueue`): Standard queue for messages about new prompts requiring example generation.
* Content Moderation (Worker):
* AWS Lambda (`ModeratePromptWorkerLambda`): Triggered by messages in `moderationQueue`.
* Pulls prompt details from DynamoDB.
* External Content Moderation Platform API: Calls the external service for analysis.
* Updates the prompt's moderation status in DynamoDB.
* Example Generation (Worker):
* AWS Lambda (`GenerateExampleWorkerLambda`): Triggered by messages in `exampleGenerationQueue`.
* Pulls prompt details from DynamoDB.
* External LLM Platform API: Calls the external LLM to generate the output.
* Updates the prompt's example field in DynamoDB.
* Content Delivery:
* Amazon S3: Stores static frontend assets.
* Amazon CloudFront: (Optional for MVP, but beneficial) CDN to serve S3 content.
* Administration:
* AWS Console: For management.
* (Minimal) Custom Admin UI: Similar to MVP 1, but potentially with separate endpoints for admin-specific actions handled by dedicated Lambdas if the complexity warrants it.
Strengths:
* Asynchronous Processing Introduced Early: Immediately decouples prompt submission from moderation and example generation, aligning better with the 5s/10s eventual performance requirements.
* Clearer Separation of Concerns: Dedicated Lambdas for API handling and background workers.
* Scalability Foundation: SQS queues and Lambda workers inherently scale.
* Direct Evolution Path: This architecture directly mirrors the asynchronous patterns of Architecture 3, making the transition smoother. The existing SQS queues and worker Lambdas can be easily adapted.
* Evolution to ECS: When containerization is desired, the worker Lambdas can be replaced with ECS Fargate tasks consuming from the same SQS queues.
* Evolution to EventBridge: SQS can be upgraded or complemented with EventBridge for more complex event routing if needed.
Trade-offs:
* Slightly More Complex Initial Setup: More AWS services (SQS) and Lambda functions to configure compared to MVP 1.
* Increased Debugging Complexity: Asynchronous flows can be harder to trace than direct synchronous calls, especially in early development.
* Higher Potential Initial Cost: While still serverless, SQS adds a small cost, and having more Lambdas might lead to more cold starts if traffic is extremely low. (Still very cost-effective overall).
---
### Comparison Summary
| Feature | MVP Architecture 1: Lambda-Centric with Direct Integrations | MVP Architecture 2: API-Driven with Dedicated Workers |
| :---------------- | :-------------------------------------------------------------------------------------- | :----------------------------------------------------------------------------------- |
| Complexity | Low: Fewer AWS services, all logic often in one main Lambda. | Medium: More AWS services (SQS), clear separation of API and worker Lambdas. |
| Development Speed | Fastest: Minimal setup, synchronous calls, direct coding. | Fast: Slight overhead for SQS/worker setup, but still quick with serverless. |
| Running Cost | Very Low: Purely serverless, minimal resources. | Low: Still serverless, but SQS adds a minor baseline cost. |
| Simplicity | High: All related operations for a prompt *can* be in one Lambda. | Medium: Clearer logical separation, but more moving parts. |
| Scalability (MVP) | Good for API, but synchronous external calls can bottleneck. | Good: Asynchronous processing inherently scales better for background tasks. |
| Evolution Path | Requires refactoring synchronous calls to asynchronous ones (add SQS/workers later). | Direct: SQS and worker pattern already established, easy to swap out Lambda for ECS. |
| Performance (MVP) | User waits for moderation/example generation (acceptable given relaxed requirements). | User gets immediate success for post; moderation/example happen in background. |
| Key Advantage | Absolute fastest to get a basic working prototype. | Provides a stronger foundation for gradual evolution to a high-performance system. |
| Key Trade-off | Monolithic Lambda, synchronous external calls for MVP. | Slightly more initial setup, slightly more complex to debug async flows. |
| Good For | Projects where "get it working first" is paramount, and performance is a later concern. | Projects that foresee the need for high-performance, decoupled systems early on. |
Prompts
- The following describes the Architectural Possibilities Pattern. It has 5 main sections: (1) Intent and Context, (2) Motivation, (3) Structure and Key Ideas, (4) Example Implementation, and (5) Consequences. Summarise it in bullet points form, section by section. [Text of the pattern]
- Provide a one-line summary of this pattern.
Source
- White, J., Hays, S., Fu, Q., Spencer-Smith, J., & Schmidt, D. C. (2023). ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design. ArXiv.org. https://arxiv.org/abs/2303.07839