Well-designed AI coding assistant prompts make development work more structured, reviewable, and repeatable. This reusable library covers codebase exploration, implementation planning, debugging, refactoring, testing, security review, documentation, and pull-request feedback, with practical guidance for adapting each prompt to ChatGPT, Claude, IDE assistants, and other models.
Overview
AI coding assistants are most useful when they receive enough context to reason about a specific engineering task. A short request such as “fix this bug” may produce a plausible answer, but it leaves important details unstated: the expected behavior, project constraints, relevant files, error output, and definition of done.
The prompt templates below use a consistent structure so you can provide that context without rewriting your workflow from scratch. Treat them as starting points rather than rigid scripts. Replace the bracketed fields, remove instructions that do not apply, and ask the assistant to state assumptions before it changes code.
For larger projects, keep prompts alongside issue templates, pull-request checklists, and test plans. This creates a repeatable AI development workflow instead of a collection of one-off conversations. If you are comparing assistants, evaluate them with the same repository context and tasks; the practical differences often become clearer when the prompt and success criteria remain constant. See the practical comparison framework for AI coding assistants for a structured way to do that.
Template structure
A reliable coding prompt usually contains six parts:
- Role and task: State what the assistant should do, such as analyze, propose, implement, test, or review.
- Context: Include the language, framework, relevant files, architecture, and the problem being solved.
- Constraints: Identify compatibility requirements, coding conventions, performance limits, security boundaries, or files that must not change.
- Inputs: Provide error messages, logs, representative data, existing code, failing tests, or expected output.
- Process: Ask for a plan, list of assumptions, risks, or questions before requesting implementation.
- Output format: Specify whether you want a patch, code snippets, test cases, a checklist, or a concise explanation.
A general-purpose template looks like this:
Task: [specific engineering objective]
Context:
- Language and framework: [details]
- Relevant files or modules: [paths]
- Current behavior: [what happens now]
- Desired behavior: [what should happen]
Constraints:
- [compatibility, performance, security, or style requirements]
- Do not change: [protected areas]
Please:
1. State important assumptions and missing information.
2. Propose a concise implementation plan.
3. Identify edge cases and risks.
4. Provide the smallest suitable change.
5. Explain how to verify it with tests or commands.
Output: [requested format]This structure is also useful for system prompt examples in team tools. Keep permanent instructions—such as repository conventions and safety boundaries—separate from task-specific details. That makes the task easier to reuse and reduces the chance that a temporary request will be mistaken for a project-wide rule.
How to customize
Good prompt engineering is less about adding length and more about removing ambiguity. Start with the smallest relevant context. A complete repository dump can overwhelm the review process, while a carefully selected set of files, interfaces, tests, and error messages usually gives the assistant a clearer working model.
For IDE assistants
Use the editor’s selected code, open files, and workspace context when available, but do not assume the assistant has inferred every dependency. Name the target file and the behavior you want changed. Ask for a diff or a proposed patch before accepting edits, particularly when the change crosses module boundaries.
For chat-based models
Paste the relevant code in labeled sections and identify the version or runtime when it affects behavior. Separate logs from source code and mark sensitive values as placeholders. If the request involves several files, provide a short dependency map rather than expecting the model to reconstruct the entire application from fragments.
For different models
Preserve the objective and acceptance criteria when moving a prompt between models. Adjust only the interaction details: how much context fits comfortably, whether the model can inspect files or run tools, and whether it returns edits in a particular format. Do not treat a confident answer as evidence that the code is correct. Compilation, tests, static analysis, and human review remain the verification layer.
For team workflows
Turn successful prompts into versioned templates. Give each one a name, intended use, required inputs, expected output, and a small set of representative examples. A prompt evaluation framework can help teams score outputs for correctness, completeness, maintainability, and adherence to constraints rather than judging them only by readability.
Examples
1. Explore an unfamiliar codebase
Review this codebase as a new maintainer. Start with the entry points, major modules, data flow, configuration, and test structure. Do not suggest code changes yet.
Inputs:
- Repository tree: [paste tree]
- Key files: [paste or link relevant files]
Return:
1. A concise architecture summary.
2. The responsibilities and dependencies of each major module.
3. Places where behavior is implicit or difficult to verify.
4. Five questions a maintainer should answer before making changes.2. Plan an implementation
Plan the implementation of [feature or change]. The current behavior is [description], and the desired behavior is [description].
Consider:
- Existing patterns in [files or modules]
- Backward compatibility
- Input validation and failure handling
- Observability and testing
First provide assumptions, an ordered plan, affected files, and risks. Do not write code until the plan is approved.3. Debug a failing test or runtime error
Help diagnose this failure. Do not guess a fix before explaining the likely cause.
Expected behavior: [description]
Observed behavior: [description]
Error or logs:
[ paste output ]
Relevant code:
[ paste code ]
Return:
1. The most likely root cause and evidence.
2. Alternative causes worth checking.
3. A minimal diagnostic sequence.
4. A proposed fix only after the diagnosis.
5. Regression tests for the failure and nearby edge cases.4. Refactor without changing behavior
Refactor [module or function] to improve [readability, duplication, testability, or separation of concerns] without changing its public behavior.
Preserve:
- Public interfaces: [list]
- Error behavior: [details]
- Performance-sensitive paths: [details]
Before proposing code, identify the current behavior that must remain unchanged. Then provide a small patch, explain the trade-offs, and list tests that demonstrate behavioral equivalence.5. Generate focused tests
Write tests for [function, endpoint, or component] using the existing project conventions.
Cover:
- Normal inputs
- Boundary values
- Invalid inputs
- Dependency failures
- Repeated calls or state changes, if relevant
Use real interfaces and avoid testing implementation details that are not part of the contract. For each test, state the behavior it protects. Flag any case that cannot be tested confidently from the supplied code.6. Perform a security review
Review this change for security risks. Focus on input validation, authorization, authentication boundaries, secret handling, injection risks, unsafe deserialization, sensitive logging, and dependency interactions.
Return findings in this format:
- Severity: [reasoned level]
- Location: [file and line or function]
- Risk: [what could happen]
- Evidence: [relevant code path]
- Remediation: [specific change]
- Verification: [test or review step]
Do not claim a vulnerability without pointing to a concrete code path or missing control.7. Review a pull request
Review this pull request against its stated objective and acceptance criteria.
Prioritize:
1. Correctness and regressions
2. Security and data handling
3. Error handling and operational impact
4. Test coverage
5. Maintainability
Ignore formatting issues unless they hide a defect. For every finding, include the location, impact, reasoning, and a practical recommendation. If no issue is found, list remaining assumptions and the tests that should still run.These are prompt engineering examples, not substitutes for engineering judgment. For applications that use retrieval, external tools, or generated code in production, connect the coding workflow to explicit evaluation and verification steps. The related guide to prompt versioning for teams covers how to track changes, tests, and rollbacks, while the prompt evaluation framework provides a broader testing mindset.
When to update
Revisit these AI coding assistant prompts whenever the surrounding workflow changes. Update them after a language or framework migration, a major repository restructure, a new test convention, or a change in deployment and security requirements. Tool changes also matter: an IDE assistant that can inspect files, run tests, or create patches needs different instructions from a chat model that receives only pasted text.
Keep a small evaluation set for important templates. Include a successful task, an ambiguous task, a known edge case, and a case where the correct response should be a question rather than immediate code. When a prompt is edited, compare the new output with the previous version and record whether correctness, clarity, and review effort improved.
To put this library into practice, choose one recurring task—such as debugging, test generation, or pull-request review. Add the relevant context fields to your issue or PR template, run the prompt on a few real examples, and refine the instructions based on observed failures. Save the stable version with an owner, revision date, and verification checklist. That simple loop turns generic prompt templates into a dependable part of an AI-assisted development process.
