AI Coding Tools: What to Specify Before They Write Code

Before asking an AI coding tool to write code, specify the goal, expected behavior, inputs and outputs, technical environment, constraints, and how success will be tested. The more clearly you describe what the program must do—and what it must not do—the less guesswork the tool has to fill in.

Start With the Outcome and Scope

Describe the result you want in terms a user can observe, rather than naming a vague task such as “make this better.” For a new feature, explain who will use it, what they need to accomplish, and where the feature belongs. For a bug fix, describe what happens now, what should happen instead, and the conditions that reproduce the problem.

Set boundaries, too. State whether the tool should change one function, add a page, update an existing service, or propose a plan before editing files. If you want a narrow patch, say so. Otherwise, a tool may reasonably make wider changes than you intended.

• Goal: “Let a user filter the order list by status.”
• Scope: “Change the existing list page; do not redesign navigation.”
• Out of scope: “Do not add bulk editing or change the database schema.”

These boundaries are especially useful in an established project, where a seemingly small feature can touch shared components, data models, or other users’ workflows.

Define Behavior With Examples

Spell out the rules the code should follow. Identify valid and invalid inputs, default behavior, edge cases, and the result for each case. Examples are often clearer than a long abstract description because they show what counts as correct.

For example: “Accept a search term of up to 80 characters. Ignore leading and trailing spaces. An empty search shows all records. If no records match, show ‘No results’ rather than an empty page.” If behavior depends on a condition, describe both sides: “When the user is signed out, show a sign-in prompt; when signed in, show the saved items.”

• What should happen with missing, malformed, or unusually large input?
• Are comparisons case-sensitive? Should whitespace be trimmed?
• What should users see when data is absent or an operation fails?
• Are there boundaries, such as inclusive date ranges or maximum file sizes?

Do not assume that a familiar word has only one interpretation. “Recent,” “fast,” “secure,” and “user-friendly” need measurable or observable criteria. If you do not know the precise rule, label the uncertainty and ask the tool to identify options rather than silently inventing a policy.

Provide the Project and Technical Context

A coding tool needs to know where its changes will run. Give relevant details such as the programming language, framework, runtime or platform, and the parts of the repository involved. In an existing project, point it to the relevant files or ask it to inspect the project structure and summarize its understanding before editing.

Include project conventions that affect the result: formatting, naming, architecture, supported browsers or operating systems, and existing libraries or APIs it should use. Mention required commands for tests, linting, or builds if they are not obvious. If a dependency should not be added, say so explicitly.

Context should be relevant, not indiscriminate. A concise description of the affected module and its interfaces is more useful than pasting a large amount of unrelated source code. Never provide passwords, private keys, access tokens, or other secrets in a prompt. Replace sensitive values with placeholders and use the project’s established secure configuration method.

State Constraints and Acceptance Tests

Constraints define what a valid solution must preserve. They can include compatibility, performance expectations, accessibility, data formats, dependencies, and limits on files or services the tool may change. Separate mandatory requirements from preferences so the tool can make sensible trade-offs.

Then explain how you will judge completion. Acceptance criteria should be observable and, where possible, testable. For example:

• Filtering by “paid” displays only paid orders.
• Clearing the filter restores the full list.
• The page remains usable with a keyboard.
• Existing tests pass, and a test covers the new empty-results state.

Tests do not replace a specification: a test can encode the wrong behavior if the requirement is unclear. Conversely, a detailed request does not prove the code works. Ask the tool to add or update relevant tests and report which checks it ran, along with any failures or checks it could not run. Review the resulting changes yourself before relying on them.

Choose How the Tool Should Work

Decide whether the tool should explain a plan, ask clarifying questions, or make a direct change. For a high-impact change or an unfamiliar codebase, asking for a short plan first can surface assumptions before they become edits. For a small, well-defined task, direct implementation may be more efficient.

You can also specify the expected response: for example, a patch, changed files, test results, and a short explanation of decisions. If the tool has access to a terminal or repository, distinguish permission to inspect files from permission to run commands or modify them. A prompt that says “inspect first, then propose changes without editing” is clearer than a general request for help.

Do not treat confident wording as evidence that the implementation is correct. Coding tools can misunderstand project-specific conventions, overlook edge cases, or produce code that appears plausible but fails when run. Review diffs, test behavior, and check that the change did not affect unrelated functionality.

Common Prompting Problems

A frequent mistake is asking for an implementation before describing the intended behavior. “Build a dashboard” leaves open its audience, data, interactions, and success criteria. Another is overloading one prompt with several unrelated tasks. Break broad work into smaller deliverables, each with its own boundaries and checks.

• Conflicting requirements: Resolve them or ask the tool to point out the conflict instead of choosing silently.
• Missing edge cases: Give examples for empty, invalid, and boundary inputs where they matter.
• Unstated assumptions: Ask the tool to list assumptions and questions before implementation.
• Unnecessary rewrites: Name files or behaviors that must remain unchanged.
• Unverified output: Require a summary of tests and review the actual code, not just the summary.

More detail is not automatically better. The aim is to remove ambiguity that affects implementation, not to prescribe every line of code. Specify required outcomes and constraints; allow the tool to choose implementation details when they do not affect those requirements.

A Reusable Request Structure

A practical prompt can be organized into a few short sections. Fill in what matters for the task and leave out irrelevant fields:

• Goal: What should the software let someone do?
• Current context: What exists now, and where should the change go?
• Behavior: What are the inputs, outputs, rules, and edge cases?
• Constraints: What must remain compatible or unchanged?
• Acceptance criteria: What observable results demonstrate success?
• Workflow: Should the tool ask questions, propose a plan, or edit directly?
• Verification: Which tests or checks should it run and report?

For instance: “In the existing order-list page, add a status filter using the project’s current UI components. It should support all, pending, and paid; default to all; and show a clear empty state when no orders match. Do not change the data model or add dependencies. First inspect the relevant files and state any assumptions, then implement the change, add focused tests, and report the checks you ran.” This gives the tool a goal, context, behavior, boundaries, and a reviewable finish line without dictating every coding decision.

Frequently Asked Questions

How detailed should a prompt for an AI coding tool be?
Include enough detail to remove ambiguity about behavior, constraints, and success. A small, isolated task may need only a few sentences; a feature spanning several components needs more context and testable acceptance criteria.

Should I tell the tool which programming language to use?
Yes, if the language or runtime is not clear from the project. In an existing repository, identify its established stack and conventions rather than asking for a different one without a reason.

Should I ask the tool to write tests?
Usually, when the project has a relevant test setup and the change has behavior that can be checked. Describe the important cases and ask it to report what it ran; still review the tests and implementation.

What if I am not sure what the requirements should be?
Say what you know, identify what is undecided, and ask the tool to list assumptions or propose options before changing code. Do not let an unstated guess become an accidental requirement.

Can I paste my whole codebase into a prompt?
Usually, it is more effective to provide the relevant files or let a repository-aware tool inspect them. Avoid sharing secrets or unrelated private data, and follow the tool and project’s data-handling rules.

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