Most developers using AI assistants are running into the same wall: the output is generic, the suggestions miss important context, and the explanations don't map to the actual codebase. The problem isn't the tool — it's the prompt. A developer who asks "fix this bug" will get a worse answer than one who provides the error stack trace, the environment, and what they've already tried.
The best AI prompts for coding work share three characteristics: they specify the role and expertise level you want the model to adopt, they provide enough context (language, framework, constraints) that the model can reason about your actual system rather than a generic one, and they structure the output so the response is actionable rather than a wall of explanation. The eight prompts below are built on this foundation — pulled from PromptSonar's Coding library, they cover the full range of tasks that make up a developer's daily work.
💡 How to use these prompts
Every placeholder in brackets — [PASTE CODE], [SPECIFY LANGUAGE] — is required. The prompts produce significantly better output the more context you provide. Don't strip context to save tokens; the specificity is what makes them work.
1
Code Review
Use case: Comprehensive review of any function, module, or pull request before it ships. Paste the code, specify the language and framework, and get a structured report covering correctness, security vulnerabilities, performance issues, code quality, error handling, test coverage gaps, and documentation needs — each issue labeled with severity (CRITICAL/HIGH/MEDIUM/LOW). More useful than a colleague skimming your PR, and faster.
You are a senior software engineer conducting a thorough code review. Review the following code for: 1) Correctness — does it do what it claims, edge cases handled, 2) Security vulnerabilities (injection, auth issues, exposed secrets, XSS), 3) Performance issues (N+1 queries, unnecessary loops, memory leaks), 4) Code quality (naming, function length, single responsibility), 5) Error handling completeness, 6) Test coverage gaps, 7) Documentation needs. For each issue found, provide: severity (CRITICAL/HIGH/MEDIUM/LOW), explanation, and suggested fix.
Language/framework: [SPECIFY]
Code:
[PASTE CODE HERE]
Why it works: The severity framework (CRITICAL/HIGH/MEDIUM/LOW) maps directly to triage priority. Without it, reviewers spend time arguing about whether something matters — with it, the review produces an ordered action list. Asking for a "suggested fix" on every finding means the output is immediately actionable, not just diagnostic.
2
Debug This Error
Use case: Root-cause diagnosis for any error — runtime exceptions, compile errors, network failures, unexpected behavior. The key is providing the full stack trace, environment details, what you were trying to do, and what you've already tried. This context prevents the model from suggesting fixes you've already ruled out and forces it to reason about the actual failure mode rather than the most common cause of the error message.
You are a senior debugging expert. Analyze this error and help me fix it:
Error message: [PASTE FULL ERROR + STACK TRACE]
Environment: [OS, runtime version, dependencies]
What I was trying to do: [DESCRIBE]
What I've already tried: [LIST]
Relevant code: [PASTE]
Provide: 1) Root cause diagnosis (not just symptoms), 2) Why this error occurs in this specific context, 3) Minimal reproduction case, 4) The fix with explanation, 5) How to prevent this class of error in future, 6) Any related issues in the code I should address while I'm here.
Why it works: "Root cause diagnosis (not just symptoms)" is the instruction that separates useful debugging output from noise. Without it, the model often describes what the error means rather than why it's occurring in your specific context. The "related issues" instruction surfaces adjacent problems that would have caused a follow-up debugging session anyway.
3
System Architecture Design
Use case: First-pass architecture design for any new system or major feature. Provide the system description, scale requirements (concurrent users, data volume), and technical constraints. The output covers service decomposition decisions, data storage strategy, API design tradeoffs, authentication architecture, deployment approach, observability plan, and failure modes — in a format that's usable as a design document or team discussion starter.
Act as a principal software architect. Design a system architecture for: [DESCRIBE SYSTEM]. Scale requirements: [CONCURRENT USERS, DATA VOLUME]. Tech constraints: [EXISTING STACK, TEAM SKILLS]. Design decisions needed: 1) Service decomposition — monolith vs. microservices with tradeoffs for this specific scale, 2) Data storage strategy (SQL vs. NoSQL, caching layer), 3) API design approach (REST vs. GraphQL vs. gRPC), 4) Authentication and authorization architecture, 5) Infrastructure and deployment strategy, 6) Observability plan (logging, metrics, tracing), 7) Failure modes and how the system degrades gracefully. Draw the architecture as ASCII or describe component interactions.
Why it works: "Monolith vs. microservices with tradeoffs for this specific scale" is the instruction that prevents the output from defaulting to microservices regardless of scale — a common AI failure mode that pushes teams toward unnecessary complexity. Providing scale requirements upfront forces the model to reason about what's appropriate rather than what's fashionable.
4
Write Unit Tests
Use case: Comprehensive unit test suite for any function or module. Specify your testing framework (Jest, pytest, JUnit, etc.) and paste the code. The output covers the happy path, edge cases (null, empty, boundary values), error conditions, async behavior, and side effects — using the AAA pattern and aiming for 100% branch coverage. Each test includes a comment explaining what scenario it tests and why it matters, which doubles as documentation.
Write comprehensive unit tests for the following code. Testing framework: [JEST/PYTEST/JUNIT/MOCHA/etc.]
Function to test:
[PASTE CODE]
Cover: 1) Happy path with representative inputs, 2) Edge cases (empty input, null, boundary values, max values), 3) Error conditions and exception handling, 4) Async behavior if applicable, 5) Side effects and mock dependencies. Use AAA pattern (Arrange-Act-Assert). Aim for 100% branch coverage. For each test, add a brief comment explaining what scenario it tests and why it matters.
Why it works: "Aim for 100% branch coverage" is the instruction that prevents the model from writing only the obvious happy-path test. Combined with the explicit edge case list, this produces a test suite that actually catches regressions rather than just demonstrating that the function runs.
5
Refactor This Code
Use case: Improving readability, maintainability, and clean-code adherence for any function or module. Describe the current issues you suspect and any constraints (API compatibility, test stability). The output provides a refactored version, explains each change and which principle it applies (DRY, SRP, etc.), flags any breaking changes or performance implications, and — importantly — identifies what you should NOT refactor and why. That last output prevents over-engineering.
Refactor the following code to improve readability, maintainability, and adherence to clean code principles. Current issues I suspect: [DESCRIBE]. Constraints: [MUST MAINTAIN API COMPATIBILITY / CANNOT CHANGE TESTS / etc.]
Code:
[PASTE CODE]
Provide: 1) Refactored version with clear diffs, 2) Explanation of each change and which principle it applies (DRY, SRP, etc.), 3) Any breaking changes or behavior differences, 4) Performance implications of the refactor, 5) What I should NOT refactor (things that look odd but exist for good reason). Show before/after side by side for the most impactful changes.
Why it works: The "what I should NOT refactor" instruction is unusual and valuable. It prevents the model from applying refactoring patterns to code that looks messy but has a valid reason for its shape — optimized hot paths, legacy compatibility constraints, intentional verbosity for operator readability. Without this instruction, aggressive refactoring output frequently breaks things.
6
Security Vulnerability Assessment
Use case: Security audit of any code or system description. Specify whether it's a web app, API, mobile app, or internal tool. The output covers OWASP Top 10 vulnerabilities applicable to your context, authentication and session management weaknesses, input validation and injection flaws, sensitive data exposure, dependency vulnerabilities with known CVEs, access control flaws (IDOR, privilege escalation), and cryptographic issues — each prioritized by exploitability × impact.
Conduct a security assessment of this code/system: [DESCRIBE OR PASTE CODE]. Context: [WEB APP/API/MOBILE/INTERNAL TOOL]. Assess for: 1) OWASP Top 10 vulnerabilities applicable to this context, 2) Authentication and session management weaknesses, 3) Input validation and injection vulnerabilities, 4) Sensitive data exposure (hardcoded secrets, logging PII), 5) Dependency vulnerabilities (outdated packages with known CVEs), 6) Access control flaws (IDOR, privilege escalation), 7) Cryptographic issues. For each finding: severity (CVSS estimate), exploitation scenario, and remediation steps. Prioritize by exploitability × impact.
Why it works: "Prioritize by exploitability × impact" is the security triage model used by professional pentesters. A critical vulnerability that requires physical access is lower priority than a medium vulnerability exploitable via a public endpoint. This instruction produces a prioritized remediation list rather than an alphabetical findings dump.
7
Performance Optimization Audit
Use case: Diagnosing and fixing performance bottlenecks in any code. Provide the code or bottleneck description, current performance metrics (if known), and your target. The output covers algorithmic complexity analysis, database query efficiency (N+1, missing indexes, over-fetching), memory allocation patterns, I/O blocking operations that should be async, caching opportunities, and unnecessary computation — prioritized by impact with before/after benchmark estimates.
Audit this code for performance issues: [PASTE CODE OR DESCRIBE THE BOTTLENECK]. Current performance: [METRICS IF KNOWN]. Expected performance target: [DEFINE]. Analyze: 1) Algorithmic complexity — identify O(n²) or worse patterns, 2) Database query efficiency (N+1, missing indexes, over-fetching), 3) Memory allocation patterns and potential leaks, 4) I/O blocking operations that should be async, 5) Caching opportunities, 6) Unnecessary computation or redundant calls. Prioritize fixes by impact. Provide benchmarks before/after for the top recommendations.
Why it works: Defining the performance target upfront ("Expected performance target: [DEFINE]") changes the framing from "what's wrong" to "what needs to change to hit this number." It stops the model from listing every micro-optimization and forces prioritization toward changes that will actually move the metric.
8
Database Schema Design
Use case: Schema design for any relational or document database. Provide the domain description, key entities, approximate scale, and database type. The output covers entity-relationship structure, table definitions with data types and constraints, index strategy (what to index and why), normalization decisions (where to denormalize for performance), foreign key and cascading rules, soft delete and timestamp handling, and migration strategy. Critically, it shows the top 3 query patterns and how the schema supports them efficiently.
Design a database schema for the following use case: [DESCRIBE DOMAIN AND KEY ENTITIES]. Scale: [APPROXIMATE ROWS, QPS]. Database: [POSTGRES/MYSQL/MONGODB/etc.]
Design: 1) Entity-relationship diagram (ASCII or description), 2) Table definitions with data types and constraints, 3) Index strategy — what to index and why, 4) Normalization decisions (where to denormalize for performance), 5) Foreign key and cascading rules, 6) How to handle soft deletes, timestamps, and audit fields, 7) Migration strategy if evolving an existing schema. Identify the top 3 query patterns and show how the schema supports them efficiently.
Why it works: "Identify the top 3 query patterns and show how the schema supports them" is the instruction that separates a textbook-correct schema from one optimized for your actual access patterns. Schema design that ignores query patterns produces correct tables that perform poorly under real load — the classic mistake made when treating schema as a data modeling exercise rather than a query optimization exercise.
Principles for Better Coding Prompts
A few patterns that apply across all eight prompts above:
- Always specify language, framework, and version. "Review this code" is weak. "Review this TypeScript code targeting Node 22 using Express 5" activates specific knowledge about that stack's idioms, common pitfalls, and security surface area.
- Provide the error context, not just the error. Stack traces without the code that caused them produce generic debugging output. Stack traces with the relevant code, environment, and what you've already tried produce root-cause analysis.
- State your constraints explicitly. "Cannot change the public API," "must run in under 100ms," "team is unfamiliar with Rust" — these constraints change the recommendations significantly. Without them, the model optimizes for the ideal solution, not the achievable one.
- Ask for tradeoffs, not just answers. For architecture and design decisions, ask "what are the tradeoffs of each approach" rather than "what's the best approach." The tradeoffs are what you need to make an informed decision.
- Request structured output explicitly. Ask for numbered lists, severity labels, before/after comparisons. Unstructured AI output on technical topics is hard to act on. The structure is what makes the response usable in a pull request or design review.
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For the foundational prompt engineering principles behind all of these, see Best Practices for Writing Effective AI Prompts. For the case on why domain-specific prompts outperform generic ones, see Why Niche-Specific AI Prompts Win. And if you're building prompts for financial analysis rather than code, see Best AI Prompts for Finance & Budgeting.