Elevating GPT Creation to New Heights: Sean Chatman's Unique Approach to AI-Powered Business Solutions
Sean Chatman
Available for Staff/Senior Front End Generative AI Web Development (Typescript/React/Vue/Python)
Available for Custom GPT creation for your business
In an era where AI-driven solutions like ChatGPT are becoming pivotal for business success, differentiating oneself in the market of GPT creators is crucial. Sean Chatman stands out in this crowded space, not just as a creator of GPTs but as a master of prompt engineering. His unique approach adds a level of detail and customization that sets his services apart from the multitude advertising on platforms like LinkedIn.
The Sean Chatman Difference
Precision in Prompt Engineering
Sean's expertise goes beyond basic GPT creation; he excels in prompt engineering. This involves crafting detailed, context-sensitive prompts that guide the GPT to produce highly relevant and specific outputs. This precision ensures that the GPTs created are finely tuned to the unique needs and nuances of each business.
Two Years of Custom AI Code Generation
Sean brings to the table two years of rich experience in custom AI code generation. This background equips him with a profound understanding of how to integrate complex AI functionalities into GPTs, making them more advanced and versatile than standard models.
Specialized Focus on Non-Technical User-Friendliness
Understanding that many business leaders and stakeholders may not have a technical background, Sean focuses on creating GPTs that are accessible and user-friendly for non-technical individuals. This approach ensures that the power of AI is available to all, regardless of their tech savviness.
Comprehensive and Continuous Support
Unlike many GPT creators who may provide a one-off service, Sean offers ongoing support and updates for the GPTs he develops. This ensures that they remain effective and relevant, adapting to new challenges and opportunities as the business evolves.
Why Sean Chatman's GPTs are a Cut Above the Rest
Conclusion
In a landscape crowded with GPT creators, Sean Chatman distinguishes himself through his unique approach, blending advanced prompt engineering with a deep understanding of business needs. His method of creating custom, adaptable, and user-friendly GPTs ensures that businesses not only adopt AI technology but do so in a way that drives real value and success.
Sean Chatman's unique approach to GPT creation is a game-changer for businesses looking to harness the power of AI. By choosing Sean, businesses are not just getting a GPT; they are getting a sophisticated AI solution, crafted with precision and foresight, ready to tackle the challenges of today and tomorrow.
Bonus
"""
Research ETL Actors
"""
import asyncio
from denz.actor import Actor
from denz.actor_system import ActorSystem
from denz.messages import Message, Command, Event
class ExtractPagesCommand(Command):
"""Command to extract content from list of urls"""
def __init__(self, urls: list):
self.urls = urls
class PagesExtractedEvent(Event):
"""Event when pages have been extracted"""
def __init__(self, pages: list):
self.pages = pages
class ScrapeActor(Actor):
"""Actor to scrape web pages"""
async def handle_extract_pages_command(self, command: ExtractPagesCommand):
"""Handles page scrape command"""
pages = []
for url in command.urls:
page = self.scrape_page(url)
pages.append(page)
await self.publish(PagesExtractedEvent(pages=pages))
def scrape_page(self, url):
"""Logic to scrape single page"""
print(f"Scraping {url}")
return "Page content"
class ParseActor(Actor):
"""Actor to parse extracted pages"""
async def handle_pages_extracted_event(self, event: PagesExtractedEvent):
"""Handles pages extracted event"""
docs = [self.parse_document(page) for page in event.pages]
await self.publish(DocumentsParsedEvent(docs))
def parse_document(self, page):
"""Logic to parse page into document"""
print(f"Parsing page into doc")
return "Clean document"
class PersistActor(Actor):
"""Actor to persist documents to database"""
async def handle_documents_parsed_event(self, event: DocumentsParsedEvent):
"""Handles parsed documents event"""
await asyncio.gather(*[self.save_document(doc) for doc in event.pages])
await self.publish(DocumentsSavedEvent(count=len(event.pages)))
async def save_document(self, doc):
"""Saves single doc to database"""
print(f"Saving document {doc}")
await asyncio.sleep(0)
class Orchestrator(Actor):
"""Orchestrates ETL workflow"""
def __init__(self):
self.scrape_actor = None
self.parse_actor = None
self.persist_actor = None
async def initialize(self):
"""Initialize dependency actors"""
self.scrape_actor = await self.actor_system.actor_of(ScrapeActor)
self.parse_actor = await self.actor_system.actor_of(ParseActor)
self.persist_actor = await self.actor_system.actor_of(PersistActor)
async def handle_etl_command(self, command):
"""Handles ETL pipeline"""
await self.scrape_actor.send(ExtractPagesCommand(command.urls))
pages = await self.wait_for(PagesExtractedEvent)
await self.parse_actor.tell(pages)
docs = await self.wait_for(DocumentsParsedEvent)
await self.persist_actor.tell(docs)
count = await self.wait_for(DocumentsSavedEvent)
print(f"{count} documents saved")
# Helper to wait on specific event
async def wait_for(event_cls):
event = await subscribe(event_cls)
return event
Integrating the create_data function with your actor framework can significantly enhance error handling and risk mitigation in your project. Here’s a strategy to combine create_data with the actor model for more robust and resilient data processing:
### Integrating create_data with Actor Framework
#### 1. Create a DataWorker Actor
This actor will be responsible for executing the create_data function. It handles data creation tasks and communicates any exceptions back to its supervisor.
```python
class DataWorkerActor(WorkerActor):
async def start_working(self, prompt, cls):
try:
# Call to create_data function
data = await create_data(prompt, cls)
# Process data or send it to another actor for further processing
except Exception as e:
await self.publish(ExceptionMessage(content=str(e)))
await self.publish(TerminationMessage(content=str(e), actor_id=self.actor_id))
```
#### 2. Modify SupervisorActor to Manage DataWorkerActor
The SupervisorActor needs to be adapted to manage instances of DataWorkerActor and handle any exceptions they raise.
```python
class SupervisorActor(Actor):
# ...
async def start_data_worker(self, prompt, cls):
self.worker_actor = await self.actor_system.actor_of(DataWorkerActor)
await self.worker_actor.start_working(prompt, cls)
```
#### 3. Use RootSupervisorActor for Monitoring
The RootSupervisorActor can monitor the entire process, handling any terminations or errors, and potentially restarting the data creation process if necessary.
```python
class RootSupervisorActor(Actor):
# ...
async def start_supervisor(self):
self.supervisor_actor = await self.actor_system.actor_of(SupervisorActor)
# Provide prompt and class for data worker
await self.supervisor_actor.start_data_worker("<prompt>", <YourPydanticModel>)
```
#### 4. Testing and Monitoring
Implement rigorous testing to ensure that the actors behave as expected in various scenarios, particularly in handling exceptions and restarts.
领英推荐
#### 5. Implementing Error Handling in the Data Creation Process
Make sure that create_data is designed to handle various types of errors gracefully, including invalid input, timeouts, and API errors.
#### 6. Logging and Observability
Implement comprehensive logging within each actor, especially in the DataWorkerActor, to track the progress of data creation tasks and capture any issues.
#### 7. Scaling and Load Management
Depending on the volume of data processing tasks, consider creating multiple DataWorkerActor instances and balancing the load among them.
### Conclusion
By integrating create_data with your actor framework, you create a more fault-tolerant and resilient system. The actor model allows for better isolation of tasks, more controlled error handling, and the ability to restart or reroute tasks in case of failures. This approach significantly reduces the risk of data processing failures impacting the overall system stability and performance.
Prioritizing risks and outlining potential worker actors for the Astra project is a strategic approach to managing and mitigating potential challenges. Below is a prioritized list of risks along with summaries for corresponding worker actors that could be implemented in the actor framework.
### Prioritized Risk List
1. Data Accuracy and Reliability:
- Highest Priority
- Risk: Inaccurate or unreliable data may lead to flawed analyses and decisions.
- Worker Actor: DataValidationWorker to verify and validate data accuracy.
2. API Limitations and Dependencies:
- High Priority
- Risk: Changes or limitations in APIs can disrupt data retrieval processes.
- Worker Actor: APIMonitoringWorker to check API status and adapt to changes.
3. Compliance and Legal Risks:
- High Priority
- Risk: Potential legal issues related to data privacy and terms of service violations.
- Worker Actor: ComplianceWorker to ensure data usage adheres to legal standards.
4. Technical Failures or Bugs:
- Medium Priority
- Risk: Software bugs or technical issues could cause system failures.
- Worker Actor: ErrorHandlingWorker to manage and log errors, and trigger alerts.
5. Performance and Scalability:
- Medium Priority
- Risk: Issues with handling high volumes of data or concurrent processes.
- Worker Actor: PerformanceOptimizationWorker to monitor and optimize system performance.
6. User Interface and Experience:
- Lower Priority
- Risk: Non-intuitive or complex UI could hinder user interaction.
- Worker Actor: UIFeedbackWorker to collect user feedback and suggest UI improvements.
### Worker Actors Summaries
1. DataValidationWorker:
- Function: Validates the accuracy and reliability of incoming data.
- Responsibilities: Performs checks against predefined quality standards, flags discrepancies.
2. APIMonitoringWorker:
- Function: Monitors the status of APIs and handles changes or limitations.
- Responsibilities: Regularly checks API endpoints, updates configurations for any changes.
3. ComplianceWorker:
- Function: Ensures all data processing complies with legal and regulatory standards.
- Responsibilities: Monitors data usage for compliance, advises on data handling practices.
4. ErrorHandlingWorker:
- Function: Manages technical errors and bugs within the system.
- Responsibilities: Logs errors, provides debugging support, and initiates recovery processes.
5. PerformanceOptimizationWorker:
- Function: Ensures optimal performance of the system under varying loads.
- Responsibilities: Monitors system performance metrics, suggests scalability improvements.
6. UIFeedbackWorker:
- Function: Gathers user feedback on the system’s interface and usability.
- Responsibilities: Analyzes user interactions, proposes UI enhancements based on feedback.
### Conclusion
By prioritizing risks based on their impact and likelihood, and aligning them with dedicated worker actors, the Astra project can effectively manage and mitigate potential challenges. Each worker actor plays a specific role in addressing a corresponding risk, contributing to the overall resilience and efficiency of the system. This approach ensures that critical areas receive the attention they need while optimizing resources across the project.