What is generative AI in software development?

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Generative AI produces text, code, images and other content from learned patterns. See how it fits coding tools, when to use it, and what to check.

Generative AI creates content from learned patterns, such as text, code, images, or audio. In AI-assisted coding, the generated content is often an explanation, a patch, or a test.

Start with AI fundamentals for the wider picture. For visual assets, image generation covers that separate workflow.

How generative AI works

You provide a request and relevant context. The model generates content, then you check it against the task. A coding agent can use tools to apply changes and run checks in a loop.

Checking generated codeA request and context produce generated code, which is checked before it is accepted or revised.Request and contextGenerate codeRun checks andreviewAccept or revise

In coding tools

In Claude Code, generation sits inside a cycle of gathering context, taking action, and verifying results. A generated patch can be followed by a test run.

For Codex, the distinction in OpenAI’s agents guide matters: the model interprets the task, while tools act on the environment. Review both the proposed text and the actions taken.

Cursor Agent can edit code and run terminal commands. Its instructions and selected model shape the work. Grok Build offers an interactive terminal and headless operation, so generated work can be part of a script as well as a conversation.

Details checked against each vendor’s documentation in November 2026.

A concrete example

Ask a coding agent:

Add a test for an empty search query. Read the existing search tests
first, follow their conventions, and run the relevant test file.

The generated test is the content. Reading the test file and running it are tool actions. Review the assertion: a passing test helps only when it checks the behavior you wanted.

Use generation for a first draft you can evaluate: tests, documentation, or a small change with a clear expected result. Give the agent that result in the prompt.

Pitfalls

A plausible patch can use an API that does not exist. Verify dependencies and run the changed flow. Generated images and audio need their own review; a coding model’s text support tells you little about those workflows.

Skill Cabinet manages reusable instructions for agents. Those instructions can shape generated work, while the checks still depend on the project.

  • LLM: An LLM generates text and code from learned patterns. Learn how the model fits inside a coding agent, with a small example and links to hands-on guides.
  • Coding agent: A coding agent uses a model and tools to read a repo, edit files and run commands in a loop. How Claude Code, Codex, Cursor and Grok Build each do that job.
  • Multimodal model: A multimodal model takes more than text, such as a screenshot, in the same request as the prompt. How Claude Code, Codex, Cursor and Grok accept an image.

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