LLM Workflows: From Automation to AI Agents with Python

Übersicht

Lehrplan

• Introduction to AI Workflows
Overview of AI agents
Introduction to Python for AI workflows
Setting up the development environment
• Basics of Automation with Python
Python scripting essentials
Automating tasks with Python
Introduction to web scraping for data collection
• Understanding Large Language Models (LLMs)
What are LLMs?
Use cases of LLMs in automation
Ethical considerations and best practices
• Connecting Python with LLMs
Using OpenAI's API with Python
Building basic language tasks with LLMs (text generation, summarization)
• Intermediate Automation Techniques
Leveraging APIs for automation
Working with JSON and data serialization
Scheduling and managing Python scripts
• Designing AI Agent Workflows
Flowcharting AI agent tasks
Integrating decision-making into workflows
Handling exceptions and errors in AI workflows
• Building a Virtual Assistant with OpenAI's Agents SDK
Overview of the OpenAI Agents SDK
Setting up and configuring the SDK
Creating a simple virtual assistant
• Enhancing the Virtual Assistant
Implementing natural language understanding
Adding conversational memory
Integrating external data sources and APIs
• Advanced Topics in AI Agents
Multi-turn conversations and dialogue management
Contextual understanding and user intent modeling
Personalization and learning user preferences
• Deploying and Scaling AI Agents
Cloud deployment options for Python applications
Monitoring and logging AI workflows
Best practices for scaling AI agents
• Conclusion and Future Directions
Emerging trends in AI agent workflows
Future opportunities with AI and automation
Final project: Build your own AI agent workflow
• Resources and Further Learning
Recommended books and articles
Online communities and forums
Tools and frameworks for advanced exploration

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Fachgebiete

Computer Science