3 AI Prompts for Developers to Plan Their Next Build

A new project often starts with an exciting but blurry idea: “I should build something for local shops.” Before opening an editor, use AI to turn that thought into a problem worth exploring, a small first version, and a list of cases the happy path misses.

These three prompts form a short planning sequence. They can help organize your thinking, but their output is a draft: they cannot verify that a problem is common, that an estimate fits your skills, or that a design is secure. Check the important assumptions with real people, your own technical judgment, and the requirements of the project.

Add a context block first

Reuse this block with each prompt. Fill in the details you know and state constraints plainly. It keeps the task grounded in your actual project instead of asking for an answer without enough context.

Stack: [your stack]
Audience: [who it's for]
Time budget: [hours/days]
Must avoid: [constraints]

For example, a small portfolio project might use Stack: React and SQLite, Audience: independent bicycle repair shops, Time budget: one weekend, and Must avoid: payments, customer personal data, and paid APIs. Adjust the block when the project changes. Do not paste passwords, API keys, private customer records, or other secrets into a prompt.

You can also ask the model to label assumptions separately from details you supplied, and to list unanswered questions instead of silently filling gaps. If you need to compare several ideas, request the same fields for each one. A short, consistent response is easier to review than a long plan that sounds complete but hides uncertainty.

1. IDEA MINER: find a problem before picking a feature

Use this when you have a broad audience or domain in mind but no clear project problem. It asks for repeatable friction and current workarounds, which gives you something concrete to investigate instead of a list of trendy app categories.

List 10 annoying, repeatable problems for [audience] in [industry]. Rank by frequency, urgency, and the workaround people use today. Suggest only ideas a solo developer could prototype in a weekend.

Treat the ranking as a set of hypotheses, not market research. The model does not know how often those problems happen for your specific audience unless you provide reliable evidence. Ask people in that audience what they do today, how often the issue comes up, and what makes the workaround costly or frustrating. If the answers do not support the idea, change the problem before investing in a polished solution.

Example: filling in the placeholders

Example input: “List 10 annoying, repeatable problems for independent bicycle repair shops in the bicycle repair industry. Rank by frequency, urgency, and the workaround people use today. Suggest only ideas a solo developer could prototype in a weekend.”

That is a starting point for discovery, not proof that repair shops want an app. Pick one answer and validate it with a short conversation or observation before you decide what to build.

2. SCOPE CUTTER: make the first version finishable

Use this after choosing a problem to explore. The prompt separates one main flow from features that can wait, and surfaces an assumption you can test early. That helps prevent a weekend prototype from quietly turning into a full product roadmap.

Turn [idea] into a 2-day MVP. Give me one core user flow, 3 must-have features, 3 features to postpone, and the riskiest assumption to test first.

Replace [idea] with a specific user and task, not just an industry label. For example: “A repair shop needs to record a bicycle's reported issue, promised pickup date, and repair status so staff can answer customer calls consistently.” Then compare the suggested scope with your stack, existing components, and two-day limit.

An MVP is not a license to skip necessary foundations. Authentication, access control, backups, privacy, and safe handling of data may be essential even when they are invisible in a demo. Ask what can be simplified without exposing users or creating data you cannot protect. Move optional dashboards, integrations, and polish to the postponed list if the core flow works without them.

3. EDGE-CASE HUNTER: inspect the moments around the happy path

Use this when you can describe a feature in a sentence. It asks for failure, empty, and confusing states before the implementation has hidden them behind a finished UI.

Review this feature: [feature]. List 8 failure states, empty states, and confusing moments. For each, state the expected behavior in one sentence.

Keep the feature description narrow. “Repair status updates” is easier to review than “the whole shop management app.” For each suggestion, decide whether it applies, what the user should see, and whether the server or data model needs to enforce the behavior. Add the accepted cases to your issue, acceptance criteria, or manual test notes; do not assume a generated list is complete.

For a repair status feature, useful questions might include what happens when a job has no status yet, two staff members update it at the same time, a save fails, or a customer follows an old status link. These are prompts for design discussion, not universal answers. Your requirements and intended users determine the expected behavior.

Run the prompts in sequence

Start with a real audience and a problem they can recognize. Use IDEA MINER to produce candidates, then verify one with people or other trustworthy evidence. Give the strongest candidate to SCOPE CUTTER, check the plan against actual time and technical constraints, and finish with EDGE-CASE HUNTER for the core flow.

The results should leave you with a short working brief: the user, the task, the smallest useful flow, postponed features, a risky assumption, and accepted edge cases. If you are still searching for a project concept, our developer project ideas beyond the todo app offers examples you can evaluate with the same sequence.

AI can suggest options and help structure a review; it cannot confirm customer demand or certify that code is correct, secure, or compliant. Check feasibility in your stack, test important assumptions directly, and review generated plans against product requirements, privacy, security, and applicable constraints before implementation.

Before coding, turn the riskiest assumption into a check you can actually perform: who can answer it, what evidence would change your mind, and what result would make you stop or revise the idea? For the technical plan, trace the main data flow and permissions yourself. A feature list is not an architecture, and missing requirements do not become safe simply because a prompt did not mention them.

Further reading

• OpenAI: Prompting — official guidance on writing, organizing, and refining prompts.
• Anthropic: Prompting best practices — official guidance on clear instructions, context, examples, and prompt structure.