Course Code: llmsforcodegen
Duration: 14 hours
Prerequisites:
  • An understanding of software development processes
  • Experience with a programming language (e.g., Python, JavaScript)
  • Familiarity with basic machine learning concepts

Audience

  • Software developers
  • Technical writers
  • Project managers
Overview:

Large Language Models (LLMs) are deep neural network models that can generate natural language texts based on a given input or context.

This instructor-led, live training (online or onsite) is aimed at intermediate-level software developers and technical writers who wish to leverage LLMs to streamline their coding workflow and create detailed, comprehensive documentation.

By the end of this training, participants will be able to:

  • Understand the role of LLMs in automating code generation and software documentation.
  • Utilize LLMs to create accurate and efficient code snippets and documentation.
  • Integrate LLMs into their software development lifecycle for enhanced productivity.
  • Maintain high-quality documentation standards using automated tools.
  • Address ethical considerations and best practices for using AI in software development.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.
Course Outline:

Introduction to LLMs in Software Development

  • Overview of LLMs and their role in code generation
  • The evolution of automated coding tools
  • Understanding the capabilities and limitations of LLMs for coding

LLMs for Automated Code Generation

  • Setting up LLMs for code generation
  • Best practices for writing prompts and interpreting LLM outputs
  • Hands-on exercises with LLMs to generate code for common patterns

Enhancing Code Quality with LLMs

  • Using LLMs for code review and bug fixing
  • Integrating LLMs with version control systems
  • Case studies on LLMs improving code efficiency

LLMs for Software Documentation

  • Automating documentation generation with LLMs
  • Ensuring consistency and completeness in documentation
  • Customizing LLMs for different documentation styles and standards

Advanced Techniques in LLMs

  • Fine-tuning LLMs for specific coding languages and frameworks
  • Developing custom LLM models for unique project needs
  • Exploring the latest advancements in LLM technology

Ethical and Legal Considerations

  • Addressing the ethical implications of automated code generation
  • Understanding the legal aspects of using LLM-generated code
  • Best practices for responsible use of LLMs in software development

Project Work

  • Implementing LLMs in a coding task
  • Peer reviews and collaborative problem-solving sessions

Summary and Next Steps

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