AI Code

Code you can read, review and defend in a pull request

Describe the behaviour you need and get an implementation with the reasoning attached, from a single function to a component, a query or a test suite.

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What is the AI Code Generator?

AI Code Generator produces code from a described requirement. It works best at the unit a developer thinks in: a function, a component, a migration, a query, a script, a test file. You state the language, the framework, the inputs and outputs and the edge cases, and it returns an implementation together with an explanation of the choices it made.

The explanation matters more than the code. Generated code that you cannot review is a liability, so the tool is built around producing something legible: named variables, obvious control flow, comments where behaviour is non-obvious, and an honest note when an approach has trade-offs.

It does not know your codebase unless you show it. Paste the surrounding types, the existing helper, the schema or the failing test and the output stops being generic. Without that context you get textbook code that ignores your conventions.

Capabilities

What you can do with the AI Code Generator

Write functions, classes, components and utilities in common languages

Generate SQL queries and schema migrations from a described data need

Produce unit tests, including edge cases you may not have listed

Explain unfamiliar code line by line

Convert code between languages or frameworks

Refactor a working but ugly implementation and say what changed

Draft regular expressions, shell scripts and configuration files

How it works

Using the AI Code Generator

01
Specify like a ticket
Language, framework version, inputs, outputs, error behaviour and constraints. "TypeScript, no dependencies, must handle empty arrays and reject negative input" produces usable code.
02
Paste real context
Include the types, the interface it must satisfy, or the neighbouring function whose style it should match.
03
Ask for tests alongside
Generating tests with the implementation surfaces misunderstandings immediately and gives you something to run.
04
Review, then run
Read the code before executing it. Check dependencies it introduced, error handling and anything touching data or credentials.

Examples

What good input and output look like

AI Code Generator

Live demoWriting the prompt1/2

You

You type

AmmarAI codes

typescriptgenerated file

Sample output — a dependency-free generic groupBy plus the Vitest file covering the edge cases named in the prompt.

Try

Utility with edge cases

Input

TypeScript function that groups an array of objects by a key selector, preserves insertion order, returns a Map, no dependencies. Include tests.

Output

A typed generic groupBy using a Map so insertion order is preserved, plus a Vitest file covering empty input, duplicate keys and numeric-string key collisions.

SQL from a question

Input

Postgres: monthly active users for the last 12 months, counting a user active if they created at least one event. Tables: users(id, created_at), events(id, user_id, created_at).

Output

A date_trunc query with a distinct user count per month, plus a note to index events(user_id, created_at) so the scan stays cheap as the table grows.

Key features

What the tool gives you

Explained output

Every generation comes with its reasoning, so the approach can be judged on merit rather than taken on faith.

Test generation

Ask for tests with the implementation and get coverage of the edge cases you named plus ones you did not.

Code explanation

Paste unfamiliar code and get a plain-language walkthrough, which is how most people onboard onto a legacy file.

Language conversion

Translate an implementation into another language while preserving behaviour and flagging what does not map cleanly.

Refactoring with a diff summary

Improve structure and get a clear statement of what changed and why.

Who it is for

People who get the most from this

Developers

Skip the boilerplate and spend attention on architecture and correctness.

Data analysts

Write SQL for questions you can describe precisely but do not want to hand-code.

Learners

See a working implementation with the reasoning, which teaches more than an answer alone.

Technical founders

Move faster on internal tooling and scripts that would otherwise never get built.

Workflows

Practical ways teams use it

Test-first bug fix

Paste the failing case, ask for a test that reproduces it, then ask for the fix and check the test passes.

Legacy file onboarding

Paste an unfamiliar module, get a walkthrough, then ask targeted questions about the parts that look risky.

Internal script

Describe a one-off data migration or reporting script and get something runnable in minutes rather than an afternoon.

Tips that improve results

State the version. Framework APIs change and unversioned prompts produce outdated patterns.
Give it the interface the code must satisfy rather than describing it in prose.
Ask what could go wrong with the generated approach; the answer is often more useful than the code.
Never paste credentials, tokens or customer data into a prompt.
Run generated tests before trusting generated code.

Mistakes worth avoiding

Shipping code you did not read because it looked confident and compiled.
Accepting new dependencies the model introduced without checking whether you need them.
Asking for a whole application in one prompt instead of building it piece by piece.
Assuming the model knows your internal conventions when you never showed it any.

AI Code Generator FAQ

Which languages does it handle well?

Mainstream languages and frameworks are strongest, since they are best represented in training data. Niche or very new frameworks produce weaker, sometimes outdated results, so verify carefully.

Is the generated code secure?

Treat it as unreviewed code from an unfamiliar contributor. It can produce insecure patterns, especially around input validation, authentication and SQL construction. Review anything touching data or credentials.

Can it work with my repository?

It works with what you paste. Provide types, schemas and neighbouring code to get output that fits your conventions instead of generic examples.

Should I use AI Chat or the code generator?

Chat is better for reasoning about an approach or debugging a problem conversationally. The code generator is better for producing an implementation, tests or a query you intend to keep.

Does it write tests?

Yes, and asking for them alongside the implementation is one of the highest-value habits with this tool.

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