An AI coding assistant uses a language model to draft, explain, or change code from natural-language instructions and nearby project files. It can suggest a whole function, create tests, or describe unfamiliar code.

Traditional autocomplete uses the language server and static analysis. It mainly suggests real symbols, methods, and types that are valid at the cursor. Its suggestions are narrow but usually grounded in the codebase.

An AI assistant can invent new code, so it is more flexible but less reliable. It may use a missing API, misunderstand a requirement, or produce insecure code. Use autocomplete as a source of known facts and treat AI output as a draft that still needs review and testing.

AI coding assistants mainly differ by where they run, how much work they can perform, and which models they support. GitHub Copilot focuses on editor and GitHub workflows. Cursor and Windsurf are AI-focused editors with codebase search and agent features. Claude Code and Aider work mainly from the terminal. JetBrains AI tools are built into JetBrains IDEs, while Continue offers an open-source, bring-your-own-model option.

Choose by workflow rather than brand. Compare editor support, repository context, model choice, privacy controls, command permissions, and pricing. An editor assistant is useful for quick suggestions while typing. A terminal agent is often better for multi-file changes, running tests, and automation. Teams should also check enterprise policy controls and whether code is retained or used for training.

Most coding assistants provide four interaction modes. Inline autocomplete shows a suggestion at the cursor, usually accepted with Tab. Chat answers questions or drafts code without changing files. Edit mode applies a requested change to selected code or a small set of files. Agent mode can search the repository, edit several files, run commands, and react to test failures.

Each mode gives the tool more freedom. Autocomplete is fast and has a small impact. Chat is useful for learning and discussing options. Edit mode is good for a focused change with a visible diff. Agent mode suits a clear, testable task, but it also has the largest risk. Select the least powerful mode that can complete the job, then review everything it produces.

Use inline autocomplete when you already know what to write and only want to type it faster. Good examples include repeated test cases, simple mappings, boilerplate, and a loop whose pattern is clear from nearby code. You can accept only the useful part and keep working without changing focus.

Use chat when the problem needs discussion or extra context. It is better for explaining an error, comparing designs, understanding a library, or planning a change across several files. A useful rule is simple: choose autocomplete when the intent is already clear, and choose chat when you still need to reason about the intent. Chat also works well for safe exploration because its answer does not change project files unless you ask it to.

AI-generated code can look polished while still being wrong. A model predicts likely text; it does not prove that the code matches your requirements. It may invent an API, miss an edge case, use an old library pattern, or create a security weakness. Generated tests can also pass while checking the wrong behavior.

You remain responsible for code that you commit. Read the full diff, confirm unfamiliar APIs, think through failure cases, and run suitable tests. Pay extra attention to authentication, data access, user input, and secrets. A helpful mindset is to treat the assistant as a fast contributor whose work always needs review. If you cannot explain a generated line or show how its behavior was verified, it is not ready to merge.

A hallucinated API or package is a name that the model invents even though it does not exist. It may suggest a believable method, configuration option, or dependency because the name fits common patterns. The generated code can appear correct until it is compiled or run.

Use the compiler, type checker, and language server as the first check. For dynamic languages, run focused tests immediately. Confirm unfamiliar methods in the official documentation for the version your project uses. Before installing a suggested package, inspect its official registry page, repository, maintainers, release history, and usage. Attackers sometimes register names that models often invent. Require human approval for new dependencies, especially when an agent can run package-install commands.

AI assistants work best on familiar patterns whose results are easy to check. Examples include boilerplate, simple data transformations, unit-test drafts, documentation, configuration, common framework code, and translations between similar languages. They can also summarize code or explain a stack trace quickly.

These tasks have many public examples, so the model has strong patterns to follow. A compiler, test, or short review can usually verify the result. Assistants are less reliable for new architecture, subtle concurrency, hidden business rules, and performance work that needs real measurements. Use them when the cost of checking the answer is low. Keep human judgment in charge when a mistake would be expensive or when correctness cannot be demonstrated with clear evidence.