Description

AI for Dev: Coding with AI

Artificial intelligence is rapidly becoming part of the software development process. Developers can now use AI to generate code, explain unfamiliar functions, troubleshoot errors, create documentation, write tests, and accelerate repetitive development tasks.

But faster coding does not automatically mean better coding.

AI for Dev: Coding with AI is a practical live webinar designed to help developers and technical professionals use AI as a productive coding assistant while maintaining security, code quality, accuracy, and human responsibility.

This course focuses on practical AI-assisted development—not replacing developers with AI.

What You’ll Learn

Participants will explore:

• How generative AI can assist with software development
• Writing better prompts for coding tasks
• Generating and refining code with AI
• Debugging errors and troubleshooting problems
• Explaining unfamiliar or legacy code
• Creating comments and technical documentation
• Generating test cases and identifying edge cases
• Refactoring and improving existing code
• Using AI to brainstorm development approaches
• Recognizing hallucinated functions, libraries, and APIs
• Reviewing AI-generated code for security and quality
• Protecting proprietary source code and company information
• Knowing when AI output requires additional research or testing

AI as a Coding Assistant

AI works best when treated as an assistant rather than an autonomous developer.

We’ll look at practical development tasks where AI can save time, including:

• Creating starter code
• Building functions from requirements
• Converting logic between programming languages
• Explaining error messages
• Reviewing code for possible problems
• Suggesting alternative approaches
• Creating SQL queries
• Generating regular expressions
• Creating sample data
• Writing unit-test ideas
• Producing documentation
• Breaking large development problems into smaller steps

The developer still remains responsible for understanding, testing, and approving the final code.

Better Prompts Produce Better Code

Simply asking AI to “write this program” often produces incomplete or unreliable results.

Participants will learn how to give AI better development context by defining:

• The programming language
• Framework or platform
• Expected inputs and outputs
• Constraints
• Existing code behavior
• Error messages
• Desired coding style
• Security requirements
• What should and should not be changed

We’ll also discuss why breaking a development task into smaller, testable requests can often produce better results than asking AI to generate an entire application at once.

Debugging with AI

AI can be particularly useful as a second set of eyes when something is not working.

Developers can use it to:

• Interpret error messages
• Trace possible logic problems
• Compare expected and actual behavior
• Identify potential syntax errors
• Explain unfamiliar code
• Suggest debugging steps
• Review functions for common mistakes

However, AI can also confidently suggest fixes that are incorrect.

The goal is to use AI to accelerate troubleshooting—not to skip understanding the problem.

Security and Proprietary Code

One of the most important issues in AI-assisted development is understanding what code and information should be shared with an AI system.

We’ll discuss risks involving:

• Proprietary source code
• Customer information
• API keys
• Passwords
• Access tokens
• Database credentials
• Connection strings
• Internal URLs
• Security configurations
• Private repositories
• Trade secrets
• Unreleased products and features

Developers should understand their organization’s policies and the AI environment they are using before submitting internal code or confidential information.

Don’t Trust Code Just Because It Compiles

AI-generated code may look professional while still containing significant problems.

Possible issues include:

• Incorrect logic
• Security vulnerabilities
• Outdated techniques
• Poor error handling
• Inefficient code
• Nonexistent functions
• Incorrect package names
• Invalid API calls
• Unsupported library versions
• Unnecessary dependencies
• Licensing or attribution concerns

AI output should be treated like code received from an unfamiliar contributor: review it, understand it, test it, and verify it before using it.

AI for Documentation and Testing

Coding is only part of software development.

AI can also assist with tasks such as:

• Writing function descriptions
• Creating README documentation
• Explaining configuration options
• Creating user-facing instructions
• Generating test scenarios
• Identifying edge cases
• Creating sample test data
• Drafting release notes
• Summarizing code changes

These uses can improve productivity while allowing developers to spend more time on higher-value technical work.

Who Should Attend?

This webinar is designed for:

• Software developers
• Web developers
• Application developers
• Database developers
• IT professionals
• System administrators who write scripts
• Technical support professionals
• Students learning programming
• Business professionals who build internal automation
• Anyone beginning to use AI for coding or development work

You do not need to be an AI engineer or machine-learning specialist.

A basic understanding of programming or scripting will help participants get the most from the session.

Code Faster—Stay Responsible

AI can dramatically accelerate certain development tasks, but speed should not come at the expense of security, accuracy, maintainability, or understanding.

AI for Dev: Coding with AI teaches participants how to use AI as a development partner while keeping the developer responsible for the final result.

Live Online Webinar

Use AI to code, debug, document, and solve problems faster—while protecting your source code and maintaining human oversight.