Course

Prompting Large Language Models in Python

Dive into the world of AI-driven conversations! Tailored for Python learners with an interest in programming and APIs, this course is your gateway to mastering generative AI APIs. You’ll learn how to create an AI-powered chatbot without the need for web development expertise. The course focuses on key areas like prompt engineering, managing conversation histories, and efficiently regulating token usage within an AI framework. By the end of this course, you’ll have a robust Python script, laying the groundwork for any chat interface.

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4.8 (359 reviews)
366 learners enrolled in this course.
  • Intermediate friendly
  • 8 hours
  • Self paced
  • 4 lessons
  • 1 project
Otavio

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Otávio Silveira

Data Analyst @ Hortifruti

Prompting Large Language Models in Python

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Course overview

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.

Key skills

  • 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

Course outline

Prompting Large Language Models in Python [4 lessons]

Introduction to the OpenAI Chat Completions API 2h

Lesson Objectives
  • Set up API credentials and authenticate with the OpenAI platform
  • Execute basic API calls for text completions
  • Understand API response structure and management
  • Identify AI-generated text use-cases in marketing

Managing Conversation Context and Token Economy 2h

Lesson Objectives
  • 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

Prompting and Response Quality 2h

Lesson Objectives
  • 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

Guided Project: Developing a Dynamic AI Chatbot 2h

Lesson Objectives
  • 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

Developing a Dynamic AI Chatbot

For this project, you’ll become a developer at a tech company, using Python and the OpenAI API to create an engaging AI chatbot. You’ll gain skills in conversation management, persona creation, and token handling as you build a chatbot that adapts to different platforms.

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