In this course, you’ll gain in-depth insights into the practical applications of large language models. Starting with the fundamentals of the OpenAI Chat Completions API, you’ll journey through creating dynamic AI-driven interactions. You’ll learn to maintain context in conversations by managing history effectively and use prompt engineering techniques to steer AI responses. Additionally, the course covers efficient token usage in scripting, ensuring your applications run smoothly. The blend of theoretical knowledge and hands-on practice in this course positions you at the forefront of AI interaction technology.
- Utilize OpenAI's Chat Completions API to generate tailored AI-driven responses
- Manage conversation histories to maintain context in AI conversations
- Create custom Python functions for dynamic interactions with large language models
- Learn prompt engineering techniques to guide AI responses effectively
- Regulate token usage within the OpenAI API framework for efficient scripting
- Adopt best practices in prompt engineering to improve the quality of AI-generated text
Prompting Large Language Models in Python [4 lessons]
- Build a chatbot using OpenAI's Chat Completions API
- Enhance the chatbot class for greater control over responses
- Implement and manage conversation history
- Integrate token management into the chatbot
- Implement persistent storage for conversation history
- Incorporate detailed context in queries to produce highly relevant responses
- Design sequential step-by-step tasks to guide AI through complicated processes
- Enhance response strategy with examples to emulate specific styles or formats
- Target specific output lengths to match content formatting requirements
- Develop a comprehensive chatbot script in Python, integrating with the OpenAI Chat Completions API
- Implement advanced conversation management strategies, including tracking conversation history and managing token usage effectively
- Employ diverse prompt engineering techniques, leveraging system messages to dictate the chatbot's behavior in different scenarios
- Incorporate zero-shot, few-shot, and chain-of-thought prompting to handle a variety of user queries
- Design and execute a series of test scenarios to evaluate the chatbot's responsiveness, coherence, and ability to maintain context
- Refine the chatbot based on testing outcomes, demonstrating iterative development and problem-solving skills
Projects in this course
Guided Project: Developing a Dynamic AI Chatbot
In this guided project, you’ll develop a dynamic AI chatbot from scratch, showcasing your ability to implement sophisticated conversation management and diverse prompt engineering strategies. The chatbot should be versatile and able to adapt to different contexts and user inputs, demonstrating potential for real-world applications.
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