ChatGPT vs Gemini vs Mistral: Workflows

Prompt
asynchronous workflow examples
ChatGPTOpenAI
asynchronous workflow examples

Asynchronous workflows are processes where tasks run independently of the main program flow, allowing other tasks to continue without waiting for the asynchronous task to complete. This is common in programming, workflow automation, and distributed systems. Below are some examples of asynchronous workflows in different contexts:

1. Web Development (JavaScript Promises / Async-Await)


async function fetchUserData() {
  try {
    let response = await fetch('https://api.example.com/user');
    let data = await response.json();
    console.log(data);
  } catch (error) {
    console.error('Error fetching data', error);
  }
}
fetchUserData();
console.log('This log happens before data fetching finishes');

Explanation: The function fetchUserData fetches data asynchronously. The rest of the code continues while the network request is pending.

2. Message Queue Based Workflow


  • Producer submits a task to a queue (e.g., RabbitMQ, AWS SQS).

  • Consumer asynchronously picks up tasks from the queue and processes them.

  • Producer does not wait for the consumer to finish.


Example Scenario: Video processing pipeline
  • Upload video → Producer places job in queue → Consumer processes video (transcoding, thumbnails) → Updates status asynchronously.


3. Asynchronous Email Sending (Backend)


from celery import Celery
app = Celery('tasks', broker='pyamqp://guest@localhost//')
@app.task
def send_email_async(email_data):
    # logic to send email
    print(f"Sending email to {email_data['to']}")
# Trigger email sending in an async way
send_email_async.delay({'to': 'user@example.com', 'subject': 'Welcome'})

Explanation: Sending emails can be time-consuming, so it’s handled asynchronously using a task queue like Celery.

4. Long-Running ETL Jobs


  • Extract data from source asynchronously.

  • Transform data while extracting.

  • Load data into database after transform completes.

  • Each step triggers the next asynchronously, allowing pipeline stages to run in parallel.


5. Asynchronous Microservices Communication


  • Microservice A sends an event/message to a Kafka topic.

  • Microservice B consumes the event and processes it.

  • Neither service waits synchronously for the other.


6. Mobile App Network Calls


URLSession.shared.dataTask(with: url) { data, response, error in
    // handle response asynchronously
}.resume()
print("Network call initiated, UI remains responsive")

The network request runs asynchronously, preventing UI blocking.

If you want examples in a specific programming language, framework, or domain, please let me know!

16:09
GeminiGoogle
asynchronous workflow examples

Asynchronous Workflow Examples

Asynchronous workflows are workflows where tasks don't have to be executed sequentially, one after the other. Instead, tasks can be initiated and then the system can move on to other tasks without waiting for the first one to complete. This improves responsiveness and scalability. Here are some examples across different domains:

1. E-commerce Order Processing:

  • Scenario: A customer places an order on an e-commerce website.

  • Asynchronous Tasks:

  • Payment Processing: Initiate the payment process with a payment gateway. The system doesn't wait for confirmation before proceeding to other tasks. A callback or message queue is used to handle success/failure notifications.

  • Order Confirmation Email: Queue a task to send the order confirmation email.

  • Inventory Update: Queue a task to update the inventory system to reflect the purchased items.

  • Shipping Label Generation: Trigger the generation of a shipping label in a separate process.

  • Notification to Warehouse: Send a message to the warehouse management system to pick and pack the order.

  • Benefits: The website remains responsive even if payment processing or inventory updates take some time. The customer immediately receives a confirmation page.


2. Image/Video Processing:
  • Scenario: A user uploads an image or video to a social media platform.

  • Asynchronous Tasks:

  • Thumbnail Generation: Generate multiple thumbnails of different sizes.

  • Video Encoding: Encode the video in different formats for various devices.

  • Content Moderation: Analyze the image/video for inappropriate content.

  • Indexing: Add the image/video to the search index.

  • Benefits: The user doesn't have to wait for all the processing to complete before seeing the image/video on their profile. The platform can handle a large number of uploads efficiently.


3. User Account Creation:
  • Scenario: A user signs up for a new account on a website.

  • Asynchronous Tasks:

  • Welcome Email: Queue a task to send a welcome email.

  • Data Analysis: Trigger a task to analyze the new user's information (e.g., location, interests) for marketing purposes.

  • Profile Creation: Create a default user profile with initial settings.

  • Benefits: The user can access the website immediately after submitting the registration form.


4. Data Backup and Archiving:
  • Scenario: Regularly backing up database data.

  • Asynchronous Tasks:

  • Data Dump: Initiate a database dump to a storage location.

  • Data Compression: Compress the backup data to save storage space.

  • Data Transfer: Transfer the compressed backup to an offsite location.

  • Archiving: Archive older data to less expensive storage.

  • Benefits: The backup process doesn't interfere with the regular operation of the database. Large datasets can be backed up without impacting performance.


5. Scientific Simulations:
  • Scenario: Running complex simulations, such as climate models or protein folding.

  • Asynchronous Tasks:

  • Dividing the Simulation: Break down the simulation into smaller, independent tasks.

  • Distribution to Compute Nodes: Distribute these tasks to multiple compute nodes or servers.

  • Data Aggregation: Collect the results from each node and aggregate them into a final result.

  • Benefits: The simulation can be run in parallel, significantly reducing the overall execution time.


6. IOT (Internet of Things) Data Processing:
  • Scenario: Receiving data from thousands of sensors.

  • Asynchronous Tasks:

  • Data Ingestion: Receive sensor data and store it in a message queue or data lake.

  • Data Validation: Validate the sensor data for errors or inconsistencies.

  • Data Transformation: Transform the data into a usable format.

  • Alerting: Trigger alerts based on sensor data thresholds.

  • Analytics: Perform analytics on the sensor data to identify trends and patterns.

  • Benefits: The system can handle a high volume of data from numerous sensors without being overwhelmed. Real-time alerts can be generated based on incoming data.


Key Technologies for Implementing Asynchronous Workflows:
  • Message Queues (e.g., RabbitMQ, Kafka, AWS SQS): Used to decouple tasks and ensure reliable delivery.

  • Task Queues (e.g., Celery, Sidekiq, AWS SQS with Lambda): Provide a framework for managing and executing asynchronous tasks.

  • Event-Driven Architectures: Build systems based on the publish-subscribe pattern.

  • Webhooks: Allow applications to receive notifications about events happening in other applications.

  • Background Jobs: Execute long-running tasks in the background without blocking the main thread.

  • Workflow Engines (e.g., Apache Airflow, AWS Step Functions): Orchestrate complex workflows with dependencies between tasks.


These examples illustrate how asynchronous workflows can improve the responsiveness, scalability, and fault tolerance of various applications. By breaking down complex tasks into smaller, independent units, systems can handle a higher volume of requests and provide a better user experience. Remember to choose the right tools and technologies based on the specific requirements of your workflow.
16:09
MistralMistral AI
asynchronous workflow examples

Asynchronous workflows are processes where tasks can be executed independently of each other, often in parallel, without waiting for the completion of previous tasks. This approach is particularly useful in scenarios where tasks can be performed concurrently to improve efficiency and reduce overall processing time. Here are some examples of asynchronous workflows:

1. Web Application Development


  • Task 1: Frontend Development

  • Task 2: Backend Development

  • Task 3: Database Design

  • Task 4: API Integration

  • Task 5: Testing


In this workflow, frontend and backend development can be done simultaneously. Database design and API integration can also be worked on concurrently, and testing can begin as soon as individual components are ready.

2. Content Creation


  • Task 1: Research

  • Task 2: Writing

  • Task 3: Graphic Design

  • Task 4: Video Editing

  • Task 5: Publishing


Research and writing can be done in parallel with graphic design and video editing. Publishing can be the final step once all content elements are ready.

3. Software Development


  • Task 1: Requirements Gathering

  • Task 2: Design

  • Task 3: Coding

  • Task 4: Unit Testing

  • Task 5: Integration Testing

  • Task 6: Deployment


Requirements gathering and design can be done concurrently. Coding and unit testing can also be performed in parallel, with integration testing and deployment following once the individual components are ready.

4. Event Planning


  • Task 1: Venue Booking

  • Task 2: Catering Arrangements

  • Task 3: Invitation Design

  • Task 4: Marketing

  • Task 5: Logistics


Venue booking, catering arrangements, and invitation design can all be handled simultaneously. Marketing and logistics can also be worked on concurrently, ensuring that all aspects of the event are covered efficiently.

5. Customer Support


  • Task 1: Ticket Creation

  • Task 2: Initial Response

  • Task 3: Problem Resolution

  • Task 4: Follow-Up

  • Task 5: Feedback Collection


Ticket creation and initial response can be handled asynchronously. Problem resolution can be worked on independently, and follow-up and feedback collection can be done once the issue is resolved.

6. Manufacturing


  • Task 1: Raw Material Procurement

  • Task 2: Production Planning

  • Task 3: Assembly

  • Task 4: Quality Control

  • Task 5: Packaging

  • Task 6: Shipping


Raw material procurement and production planning can be done in parallel. Assembly, quality control, and packaging can also be handled concurrently, with shipping being the final step.

7. Marketing Campaign


  • Task 1: Market Research

  • Task 2: Content Creation

  • Task 3: Ad Design

  • Task 4: Social Media Planning

  • Task 5: Email Campaign

  • Task 6: Analytics


Market research, content creation, and ad design can be done simultaneously. Social media planning and email campaigns can also be worked on concurrently, with analytics being the final step to measure the campaign's success.

8. E-commerce Operations


  • Task 1: Product Sourcing

  • Task 2: Inventory Management

  • Task 3: Website Maintenance

  • Task 4: Customer Service

  • Task 5: Order Fulfillment

  • Task 6: Shipping


Product sourcing and inventory management can be handled in parallel. Website maintenance and customer service can also be worked on concurrently, with order fulfillment and shipping being the final steps.

9. Project Management


  • Task 1: Project Planning

  • Task 2: Resource Allocation

  • Task 3: Task Assignment

  • Task 4: Progress Tracking

  • Task 5: Risk Management

  • Task 6: Reporting


Project planning and resource allocation can be done simultaneously. Task assignment and progress tracking can also be handled concurrently, with risk management and reporting being ongoing tasks.

10. Data Analysis


  • Task 1: Data Collection

  • Task 2: Data Cleaning

  • Task 3: Data Transformation

  • Task 4: Model Training

  • Task 5: Model Evaluation

  • Task 6: Reporting


Data collection, cleaning, and transformation can be done in parallel. Model training and evaluation can also be handled concurrently, with reporting being the final step.

These examples illustrate how asynchronous workflows can be applied across various domains to enhance productivity and efficiency.

16:09

💡 Try this comparison yourself:Compare AI models side-by-side on SNEOS

Analysis

This comparison demonstrates the different approaches each AI model takes when responding to the same prompt. Here are the key differences observed:

Response Characteristics

ChatGPT: Provides a direct response with 38 sentences.

Gemini: Provides a direct response with 68 sentences.

Mistral: Provides a detailed response with 33 sentences.

Key Takeaways

  • Each model brings unique strengths to this type of query
  • Response styles vary significantly between models
  • Consider your specific use case when choosing between these models

Try This Comparison Yourself

Want to test these models with your own prompts? Visit SNEOS.com to compare AI responses side-by-side in real-time.

This comparison was generated using the SNEOS AI Comparison ToolPublished: October 01, 2025 | Models: ChatGPT, Gemini, Mistral