Quick Start to Prompt Engineering for Software Developers Turkmenistan Eğitimi

Become a Prompt Engineering Pro: Supercharge Your Coding Skills, Development Efforts and Processes with AI

You should have incoming skills aligned with the topics in the course(s) below, or should attend as a pre-requisite:

• Building Intelligent Applications with AI and ML – Level 1
• Python Foundations

To gain the most from attending this course you should possess the following incoming skills:

• Basic knowledge of programming concepts and syntax in Python.
• Familiarity with common data formats such as CSV, JSON, and XML.
• Experience using command-line interfaces and basic text editing tools.
• Understanding of basic machine learning concepts and algorithms.

Working in an interactive learning environment, led by our engaging expert, you will:

• Gain a solid understanding of prompt engineering concepts and their applications in software development and AI-driven solutions.
• Master the techniques for preprocessing and cleaning text data to ensure high-quality inputs for AI models like GPT-4.
• Develop expertise in GPT-4 tokenization, input formatting, and controlling model behavior for various tasks and requirements.
• Acquire the ability to design, optimize, and test prompts effectively, catering to diverse business applications and use cases.
• Learn advanced prompt engineering techniques, such as conditional text generation and multi-turn conversations, to create more sophisticated AI solutions.
• Practice creating prompts to generate, run, and test code in a chosen programming language using GPT-4 and OpenAI Codex.
• Understand the ethical implications and best practices in responsible AI deployment, ensuring fair and unbiased AI applications in software development.

1. Introduction to Prompt Engineering
   • Overview of prompt engineering and its importance in AI applications
   • Major applications of prompt engineering in business
   • Common challenges faced in prompt engineering
   • Overview of GPT-4 and its role in prompt engineering
   • Key terminology and concepts in prompt engineering
2. Getting Things Ready: Text Preprocessing and Data Cleansing
   • Importance of data preprocessing in prompt engineering
   • Techniques for text cleaning and normalization
   • Tokenization and n-grams
   • Stop word removal and stemming
   • Regular expressions and pattern matching
   • Lab: Hands-on exercise using Python and the NLTK library to preprocess and clean a sample dataset.
3. GPT-4 Tokenization and Input Formatting
   • GPT-4 tokenization and its role in prompt engineering
   • Understanding and formatting GPT-4 inputs
   • Context windows and token limits
   • Controlling response length and quality
   • Techniques for handling out-of-vocabulary tokens
   • Lab: Practice tokenizing text and formatting inputs using the GPT-4 Python API.
4. Prompt Design and Optimization
   • Master the skills to design, optimize, and test prompts for various business tasks.
   • Designing effective prompts for different tasks
   • Techniques for prompt optimization
   • GPT-4 system and user parameters for controlling behavior
   • Importance of prompt testing and iteration
   • Best practices for prompt engineering in business applications
   • Lab: Create and optimize prompts for a sample business task using the GPT-4 Python API.
5. Advanced Techniques and Tools in Prompt Engineering
   • Learn advanced techniques and tools for prompt engineering and their integration in business applications.
   • Conditional text generation with GPT-4
   • Techniques for handling multi-turn conversations
   • Overview of tools for prompt engineering: OpenAI API, OpenAI Codex, and OpenAI Playground
   • Integration of GPT-4 with other software platforms and tools
   • Monitoring and maintaining prompt performance
   • Lab: Implement a multi-turn conversation with GPT-4 using OpenAI Codex and OpenAI Playground.
6. Code Generation and Testing with Prompt Engineering
   • Develop the skills to generate, integrate, and test AI-generated code effectively, enhancing productivity and creativity in software development projects.
   • Introduction to code generation with AI models like GPT-4
   • Designing prompts for code generation across programming languages
   • Techniques for specifying requirements and constraints in prompts
   • Generating and interpreting code snippets using AI-driven solutions
   • Integrating generated code into existing projects and codebases
   • Best practices for testing and validating AI-generated code
   • Lab: Practice creating prompts to generate, run, and test code in a chosen programming language using GPT-4 and OpenAI Codex.
7. Ethics and Responsible AI
   • Understand the ethical implications of prompt engineering and the importance of responsible AI deployment in business.
   • Ethical considerations in prompt engineering
   • Bias in AI systems and its impact on prompt engineering
   • Techniques to minimize bias and ensure fairness
   • Best practices for responsible AI deployment in business applications
   • Monitoring and addressing ethical concerns in prompt engineering