ChatGPT Prompts for Developers: Plan, Debug, and Review

Useful ChatGPT prompts for developers define the expected behavior, show the relevant code and evidence, and require a way to check the answer. Start with a bounded task, not a request to improve an entire application. The templates below cover requirements, debugging, implementation planning, and code review.

A project record in fenbs keeps reviewed prompt procedures and project rules available to connected assistants. Your next session can start with the actual constraints and unfinished checks instead of another reconstruction of the last debugging conversation.

What context does a developer prompt need?

Supply the language and framework versions, relevant interfaces, expected behavior, actual behavior, and the smallest code sample that preserves the problem. Include repository instructions that affect the change. Redact secrets and private records before sharing logs or examples, and use synthetic values when the original data is unnecessary.

Say what access the assistant has. In an ordinary chat, it can propose commands but cannot establish that they ran in your environment. In a connected coding tool, specify the permitted files and operations. A prompt does not give a chat automatic access to your repository, terminal, or test results.

OpenAI’s prompt engineering documentation↗ (opens in a new tab) describes separating instructions, examples, and supporting context with clear structure. For developers, that separation helps prevent a copied error message or source comment from being confused with a request. The AI prompts hub collects other work templates.

• Goal: the observable behavior a person needs.
• Boundary: files and interfaces that may change, plus work explicitly outside the task.
• Evidence: a reproduction, error output, and relevant code with stable labels.
• Verification: checks you can run and the behavior each should demonstrate.

How do you turn a vague feature request into requirements?

Ask for questions and acceptance examples before asking for a patch. A request such as “make search better” leaves sorting, matching, empty results, and permissions unresolved. Choose the user-visible behavior first so the assistant does not convert a small request into a speculative redesign.

Help clarify this change before implementation.
User problem: [problem]
Current behavior: [behavior]
Requested behavior: [behavior]
Relevant interfaces and constraints: [details]
Permitted scope: [files and operations]
Out of scope: [boundaries]

Return a short requirements summary, essential questions, and acceptance examples. Cover normal input, empty input, invalid input, and permission failures where relevant. Label assumptions. Do not choose unresolved product behavior or invent repository conventions. Suggest the smallest implementation approach only after identifying the open decisions.

Resolve questions that change the outcome and keep the answer with the task. A developer can choose routine implementation details, but the assistant should not silently decide whether a restricted record becomes visible in search results. Identify who owns that behavior and preserve their answer.

Record durable project choices in fenbs Decisions and rules. A rule such as “permission failures must not expose record names” should reach every later assistant, while the decision to change one search label belongs with that particular task.

Which prompt makes debugging reproducible?

Give a failing example before suggesting a cause. Include exact input, observed output, expected output, and commands you actually ran. Ask the assistant to rank hypotheses by evidence and propose a discriminating check: an observation that would support one explanation while weakening another.

Investigate this reproducible failure.
Environment and versions: [details]
Minimal relevant code: [code]
Reproduction steps and exact input: [steps]
Expected result: [result]
Actual result and error output: [evidence]
Checks already run with results: [checks]

Separate observations from hypotheses. Identify the smallest next check that distinguishes the leading causes. Explain what each possible result would mean. Do not propose a broad rewrite. If the evidence is insufficient, ask for the specific missing output. Never state that you ran a command unless you actually ran it in the available environment.

For a hypothetical date filter, test a known boundary value and a nearby value under the same time zone. A useful answer should connect those results to the suspected comparison behavior. “Probably a time zone bug” is a hypothesis; a repeatable mismatch with recorded inputs is evidence.

Return the actual check result to the conversation. If it contradicts the theory, discard the theory rather than requesting another version of the same patch. For broader task framing, the work prompt examples show how to name a deliverable and the evidence needed to complete it.

How do you request a small patch with useful checks?

Once the cause is supported, ask for the smallest change that corrects it while preserving surrounding behavior. Include constraints on dependencies, public interfaces, generated files, and deployment. Give the actual project commands instead of inviting the assistant to guess what your repository uses.

Address the confirmed issue below.
Confirmed cause and supporting evidence: [details]
Approved behavior: [requirements]
Allowed files: [paths]
Project rules: [rules]
Relevant verification commands: [commands]

Propose the smallest patch and explain why it fixes the reproduction. Preserve existing interfaces unless a change is explicitly required. Include a regression check that fails for the original behavior and passes for the correction. If you have execution access, report exact checks and outcomes. Otherwise label them as proposed. Stop before operations outside the permitted scope.

Inspect the diff before running it in a consequential environment. Check that the fix addresses the failing case without removing validation, weakening access control, or swallowing an error. A passing command is useful evidence only for the behavior that command actually exercises.

The separate ChatGPT and Codex guide covers the relationship between conversation and coding tools. These ChatGPT prompts for engineers apply to the task specification either way; the available tools determine which checks the assistant can perform itself.

What should you ask during code review?

Give the requirements and diff together. Ask for concrete findings with a triggering input or execution path, the likely consequence, and a focused correction. Separate verified defects from questions and optional improvements, so stylistic suggestions do not obscure a behavior that could break users.

Review this change against the requirements.
Requirements and project rules: [details]
Diff and relevant surrounding code: [code]
Checks performed with actual results: [results]

Find correctness, access-control, data-loss, and compatibility problems supported by the supplied code. For each finding, give the location, triggering case, consequence, and evidence. Label uncertainty. Separate blockers, questions, and optional improvements. Do not claim coverage that the recorded checks do not provide. If you find no supported defect, say so and name the remaining verification limits.

Have a person evaluate the findings and run the relevant checks. OpenAI’s model optimization guide↗ (opens in a new tab) notes that model output can vary and recommends measuring behavior against representative inputs. Here, that means retaining a few meaningful examples when you revise your review prompt, not treating one impressive answer as proof.

How do you preserve a prompt that worked?

Save the reusable procedure with its required inputs, a minimized example, and the checks that exposed a past failure. Keep project-specific facts separate so an old framework version or temporary workaround does not become a permanent instruction. Recheck the prompt when the tool, model, or repository conventions change.

Retain an example where the right response is to request more evidence. That case checks whether the template encourages confident guesses when a reproduction is incomplete. Also keep a clear success case, so a cautious revision does not make the assistant ask unnecessary questions when the task is already well specified.

Our system prompt examples explain how durable instructions differ from a one-off request. Use that distinction for the shared library: “report commands honestly” is reusable, while “this branch currently fails the date-filter check” belongs in the task’s current progress.

How to do this with fenbs

1. Create a free account and add the software project you are working on.
2. Save these procedures in Skills and prompts, with required inputs and a small reviewed example.
3. Record decisions people made about scope, interfaces, and safety boundaries in Decisions and rules.
4. Ask your connected assistant to read the project context and relevant prompt before investigating a task.
5. Save the actual verification results and the remaining work in the task and Where we left off before the session ends.

fenbs helps preserve the work around a code change. It is not the right fit if you only need somewhere to execute tests: it does not run your application or replace version control. Keep code and executable checks in the repository, with concise progress and source references in the shared record.