An AI coding assistant can produce a convincing PHP controller in seconds. It can also copy an unsafe database query, repeat a muddled abstraction, or add a new endpoint that quietly ignores the conventions your team relies on. The difference often lies in the examples it sees.

Prompts matter, but they are temporary. Your repository is the persistent teaching material: code, tests, documentation, configuration, and the accumulated decisions behind them. If those materials are clear and consistent, an assistant has a better chance of producing a useful first draft. If they are contradictory, a longer prompt may only give the confusion better punctuation.

Your repository is a working curriculum

A coding assistant does not automatically know which patterns are intentional and which are leftovers from a migration. It may encounter three ways to handle validation and have no reliable basis for choosing among them. A human joining the team faces the same problem, although they can ask a colleague why the oldest example is misleading.

Start by making the preferred path easy to find. A small, well-maintained example of an API endpoint can teach more than a broad instruction to “follow best practices.” It can show where validation happens, how a service reports a business failure, what response shape clients receive, and which test demonstrates the contract.

This does not mean forcing every feature into one template. A read-heavy reporting endpoint and a payment operation have different needs. The goal is to make the reasons for those differences visible, so that both developers and assistants can distinguish a deliberate exception from an accident.

Make the right example complete

Consider an endpoint that creates an order. Its useful teaching material extends beyond the controller. A complete example should connect the request contract to the application service, persistence, error handling, and tests. If order creation and inventory reservation must succeed together, the transaction boundary should be clear in the code that owns that decision.

For example, a service might express that boundary like this:

public function placeOrder(PlaceOrder $command): Order
{
    return $this->transactions->run(function () use ($command): Order {
        $order = $this->orders->create($command);
        $this->inventory->reserve($order);

        return $order;
    });
}

The example is useful only if the surrounding implementation makes its promises true. The transaction runner must roll back when reservation fails, and both repositories must participate in the same database transaction. A test should verify that failure leaves no order behind. If reservation calls an external service, a database transaction alone cannot roll that call back; the design needs an explicit strategy for partial failure.

That distinction matters when asking an assistant to add similar behavior. The visible method suggests an atomic operation, but the actual guarantee depends on infrastructure outside the snippet. Give the assistant the relevant implementation and tests, and ask it to identify assumptions before extending the pattern.

Write constraints where changes happen

Good repository guidance is specific enough to influence a decision. “Keep APIs secure” is worthy but vague. “Check that the authenticated user can access the requested account before returning its invoices” gives a developer something concrete to implement and review.

Put durable guidance near the work it governs. An API reference should describe request and response fields, authorization expectations, and meaningful error responses. A database migration guide should explain how the team handles changes during rolling deployments. A short architecture note can record why a boundary exists and when an exception is justified.

Keep these documents aligned with the implementation. An obsolete example can be more damaging than no example because it offers false confidence. When a convention changes, update the representative code and its documentation together; otherwise, the repository teaches two competing lessons.

Use tests to express behavior, not just coverage

Tests give an assistant concrete examples of what the system promises. A useful API test might show that a valid request creates a resource, that another account cannot access it, and that an invalid request receives a documented error. Those examples help preserve behavior when the implementation changes.

Tests also have limits. A passing suite does not prove that a new query is efficient, that retries are safe, or that an authorization rule covers every path. Review those properties directly. For a retryable operation, for instance, ask what happens if the first request succeeds but the response is lost. If the client retries, can the operation create a duplicate? An idempotency design may be needed, along with tests for repeated requests and concurrent attempts.

Give the assistant a bounded assignment

Once the repository provides good examples, a focused prompt can point to them. Name the behavior to add, the relevant existing code, the constraints that matter, and how success will be checked. For example:

Add an endpoint to list invoices for the authenticated account. Follow the existing pagination and error response conventions. Ensure the query cannot return another account’s invoices, and add tests for access control and pagination. Flag any missing assumptions.

This gives the assistant a target without prescribing every line. Review the proposed query and tests yourself: pagination needs a stable ordering, and filtering by account must happen before results are returned. If performance matters, inspect the query plan against representative data and decide whether an index is justified.

Teach operational reality too

Application code is only part of the lesson. Docker configuration, environment variables, and deployment notes tell the story of how the service runs. Clear setup instructions help an assistant suggest changes that fit the development environment, while an accurate deployment guide exposes dependencies between application releases and database migrations.

For a schema change, document the compatibility window: which application versions must work with which schema, and when old columns or code paths can be removed. Avoid assuming that every container switches versions at once. Where a migration could lock a large table or take substantial time, assess it for the database and workload in use before scheduling the rollout.

The best prompt cannot compensate for a repository that hides its decisions. Invest in examples that work, tests that explain consequences, and documentation that stays close to reality. You will give an AI assistant better material to learn from, and you will give every developer on the team a clearer place to start.