How to Give Better Instructions to an AI Coding Tool

You do not need to speak like a developer to build software with AI. You do need to explain what success looks like.

A request such as “make the dashboard better” leaves many choices open. The AI may change the layout, data, colors, or navigation. It can produce a lot of code and still solve the wrong problem.

Why does “make it better” fail?

An AI coding tool can inspect files and find patterns. It cannot know which business choices matter to you unless you say them.

Imagine that customers leave your checkout page before paying. “Improve checkout” could mean fewer fields, a new design, a different payment service, or better error messages. Those are different jobs with different risks.

Your first task is not to describe the code. It is to describe the customer’s problem and the result you want.

What should every coding request include?

Most useful requests contain six parts. You can write each part in plain English.

GitHub’s guidance for coding agents also recommends a clear problem, a well-scoped task, and complete acceptance criteria. Acceptance criteria are simply the checks that prove the work is complete. See GitHub’s coding-agent guidance.

What does a useful request look like?

Here is a request that forces the AI to guess:

Make the invoice page better and add PDF downloads.

Here is the same job with a clear boundary:

Context
This app helps freelance designers send invoices to clients.

Goal
Add one Download PDF action to the invoice detail page.

Current behavior
Clients can view an invoice in the browser but cannot save it.

Expected behavior
When a client opens an invoice and selects Download PDF, the app saves a PDF that contains the same client name, items, total, and due date.

Must not change
Do not change invoice calculations, payment status, login, or who can view an invoice.

Done when
Test one paid and one unpaid invoice. Both PDFs must match the page. A client must not be able to download another client's invoice.

First inspect the existing invoice page and access rules. Explain your plan before changing code. After the change, run the relevant checks and report the evidence.

This version does not tell the AI which library or file to use. The tool can discover those details. You keep control of the outcome.

Which choices should you make yourself?

You should decide choices that affect the customer or the business. Let the AI propose technical details after it inspects the project.

• You decide: who can use the feature, what they can do, what it should look like, and what must stay unchanged.
• The AI can propose: which existing component to reuse, where the logic belongs, and which tests fit the project.
• You approve first: new paid services, database changes, deleting data, changing login, or replacing a working system.

Official OpenAI guidance calls these approval boundaries. It recommends giving the model the goal, domain context, hard limits, and success criteria while avoiding unnecessary step-by-step control. Read the official OpenAI model guidance.

What if you do not know an important detail?

Do not hide the gap. Ask the AI to help you make the decision before it builds anything.

I know the customer outcome, but I may have missed important choices. Ask me up to five plain-English questions that would change the feature or its risks. Do not edit the project yet. If a choice is technical only, recommend a default and explain it in one sentence.

Good questions may reveal whether guests can use the feature, what happens on mobile, or which records must stay private. Five focused questions are more useful than a long technical interview.

Which prompt can you copy for your next feature?

Context
This app helps [type of user] do [main job].

Goal
For this task, implement only [one visible result].

Current behavior
Right now, when the user [action], [what happens now].

Expected behavior
When the user [action], [what should happen].

Must not change
Keep [working feature, data, design, or access rule] unchanged.

Approval needed
Ask before adding a paid service, changing the database structure, deleting data, or changing login and payment behavior.

Done when
1. [User action] produces [visible result].
2. [Important failure case] shows [safe result].
3. Existing [related feature] still works.

Inspect the relevant parts of the project first. Explain the plan in plain English. Make only the needed changes. Run the available checks, then report what changed, what passed, and what I still need to test.

How do you know the instruction worked?

A polished answer is not proof. Before you accept the work, check that the AI did all of the following:

• Repeated the goal in the same way you meant it.
• Named what it would change and what it would leave alone.
• Asked about a business choice instead of silently inventing one.
• Changed only the requested feature.
• Ran a check that can pass or fail.
• Reported anything it could not verify.

If the AI misunderstood the goal, stop before asking for more code. Correct the goal and expected behavior first. More code will not repair a confused instruction.

What should you read next?

• Ask AI to inspect your app before it writes code.
• Break a large idea into small AI tasks.
• Learn what vibe coding can and cannot do for you.

Once the feature works, it still needs a dependable place to run. See how JustDeploy helps you publish and operate an AI-built app.