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Introduction to Generative AI for Software Development

Laurence Moroney

4.6

Based on 565 ratings

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Laurence Moroney

This course is designed to enhance your skills by integrating AI chatbots into your development process. You’ll learn about how large language models (LLMs) work and how this general-purpose technology can be applied to common software development tasks to help boost productivity, creativity, and support you in your career.

By the end of this course, you will be able to:

- Understand the differences between machine learning and traditional software development

- Describe how large language models generate text

- Prompt an LLM to assist in the tasks that make up the software developer role

By the end of this course, you will be able to:

- Understand the differences between machine learning and traditional software development

- Describe how large language models generate text

- Prompt an LLM to assist in the tasks that make up the software developer role

- Guide an LLM to complete a task in a specific way by writing detailed prompts and iterating to improve output

- Leverage the depth of software development knowledge encoded in an LLM by prompting it to assume specific job roles or personas

- Write code quickly using an LLM as a pair-coding partner

- Analyze code for efficiency, security, and performance using an LLM

By knowing how machine learning systems work, and an understanding of how they can be applied in software development, you’ll be able to use them more effectively to support your growth and work as a developer or engineer.

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Develops skills in prompting AI chatbots for software development tasks

Teaches underlying concepts of machine learning for software development

Helps learners improve productivity and creativity in software development

Provides a comprehensive overview of AI chatbots in software development

Offers hands-on practice in using AI chatbots for software development

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Reviews summary

Generative ai for developers: practical llm use

According to students, this course offers a solid introduction to integrating Generative AI into the software development workflow. Many highlight the invaluable prompt engineering techniques and the practical, hands-on approach, particularly the pair-coding with LLMs and code analysis sections, as transformative for daily tasks. Learners appreciate the clear explanations and well-structured content provided by the instructor. However, some experienced developers found certain parts too basic or felt it scratched the surface, desiring deeper dives into LLM architectures or API integrations beyond prompting. A few remarks suggest the field's rapid evolution means content can feel quickly outdated.

Well-organized content with clear explanations and demos.

"The concepts are explained well, and the instructor's explanations are clear and concise."

"The course is well-structured, and the demos are very clear, helping to solidify my understanding."

"The videos are high quality, and the quizzes reinforced learning effectively."

Provides effective techniques for interacting with LLMs.

"The prompt engineering techniques taught here are truly invaluable."

"I feel much more confident using these tools now, especially in crafting better prompts."

"This course is a fantastic starting point for any developer looking to integrate AI, emphasizing powerful prompting."

Focuses on immediately applicable skills for developers.

"I immediately applied what I learned to my daily coding tasks, it's a game-changer for my workflow."

"The sections on pair-coding with LLMs and code analysis were incredibly useful for my work."

"I learned how to use practical tools and strategies that I could apply immediately to my work."

The rapidly evolving field means content can quickly age.

"The content felt a bit outdated by the time I finished, which is a big problem in AI."

"While the core concepts are useful, the industry moves so fast that some parts quickly become less relevant."

"It covers the basics, but some examples felt a bit simplistic for real-world application without further exploration."

Some expected more on building applications, not just using LLMs.

"Not what I expected for 'software development'; I hoped for more on how to build applications *with* LLMs."

"It felt more like a prompt engineering 101 than a course for software development in terms of API usage."

"I was disappointed, many techniques discussed are widely known, and I expected more sophisticated strategies or actual API integrations."

May be too foundational for highly experienced professionals.

"Good course for beginners, but I found some parts a bit basic for an experienced developer."

"I wished for more advanced techniques or deeper dives into specific LLM architectures and API integrations."

"It feels like it scratches the surface rather than diving deep; I wanted more."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Introduction to Generative AI for Software Development with these activities:

Review Software Development Fundamentals

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Strengthen your foundational understanding of software development concepts before diving into AI chatbots.

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Revisit core programming principles, such as data structures, algorithms, and object-oriented programming.
Refresh your knowledge of a programming language, such as Python, which is commonly used for AI chatbots.

Engage in LLM-Focused Study Groups

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Collaborate with peers to share knowledge, experiences, and best practices related to LLMs.

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Form a study group with other learners interested in LLMs.
Establish regular meetings to discuss LLM concepts, applications, and challenges.
Share resources, articles, and project ideas with the group.
Provide feedback and support to other members of the group.

Practice Prompts for Large Language Models

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Sharpen your prompt engineering skills by practicing writing effective prompts for LLMs.

Browse courses on AI Chatbots

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Start with a clear goal in mind for the LLM's output.
Use relevant keywords and provide enough context.
Break down complex requests into smaller, more manageable tasks.
Experiment with different variations of the same prompt.

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Develop a Software Development Plan with LLM Integration

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Enhance your software development process by creating a plan that incorporates the use of LLMs.

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Analyze your current software development workflow.
Identify areas where LLMs can complement or enhance your existing tools and techniques.
Develop a phased plan for integrating LLMs into your workflow, starting with low-risk tasks.
Monitor the effectiveness of LLM integration and make adjustments as needed.

Explore Code Generation with LLMs

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Enhance your understanding of how LLMs can generate code by following guided tutorials.

Browse courses on Code Generation

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Identify a specific coding task you want the LLM to perform.
Choose a tutorial that aligns with your skill level and the complexity of the task.
Follow the tutorial step-by-step, experimenting with different LLM models and prompts.
Analyze the generated code, comparing it to manually written code in terms of efficiency and performance.

Contribute to LLM-Related Open-Source Projects

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Gain practical experience and contribute to the LLM community by participating in open-source projects.

Browse courses on Open Source

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Identify open-source projects that leverage LLMs or focus on LLM-related topics.
Choose a project that aligns with your interests and skills.
Review the project's documentation and contribute code, documentation, or other resources.
Engage with the project's community through forums or discussion boards.

Build an LLM-Assisted Development Project

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Apply your LLM skills in a practical setting by building a project that leverages LLMs.

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Define the scope and goals of your project.
Identify specific tasks within your project where LLMs can provide assistance.
Develop a strategy for integrating LLM-generated content into your project.
Evaluate the effectiveness of the LLM's contributions to your project.

Career center

Learners who complete Introduction to Generative AI for Software Development will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Introduction to Generative AI for Software Development.

Hands-On Machine Learning with Scikit-Learn, Keras,...

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The premier applied machine learning masterwork, taking practitioners through intuitive concept breakdowns, production-grade Scikit-Learn workflows, and deep neural architecture implementations.

Introduction to Generative AI for Software Development

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Reviews summary

Generative ai for developers: practical llm use

Activities

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