This article covers four reusable prompt patterns that improve output quality for common development tasks: code generation, debugging, code review, and documentation. Each pattern includes a template you can adapt.

Pattern 1: Context-Bounded Code Generation

When asking an LLM to generate code, providing explicit context boundaries produces more accurate and maintainable results.

Role: You are a senior [LANGUAGE] developer.
Task: Implement [FEATURE DESCRIPTION] in [LANGUAGE/Framework].
Context:
- Existing codebase uses [FRAMEWORK VERSION]
- Coding conventions: [CONVENTIONS, e.g., functional style, no external deps]
- Input: [DESCRIBE INPUT TYPES]
- Output: [DESCRIBE EXPECTED OUTPUT TYPE]

Constraints:
- Handle edge cases: [LIST EDGE CASES]
- Do not use external libraries unless specified
- Include inline comments for non-obvious logic
- Return only the function implementation, no surrounding explanation

Output format: A single code block in [LANGUAGE].

The key elements are role specification, explicit input/output contracts, and constraints on scope. Without these, models tend to add unnecessary dependencies or produce code that does not fit your existing architecture.

Pattern 2: Structured Debugging Assistant

Debugging with an LLM works best when you provide the error, the code, and your debugging context separately rather than pasting everything at once.

Role: You are a debugging assistant for [LANGUAGE] applications.

Error message:
[EXACT ERROR TEXT]

Relevant code:
[CODE SNIPPET - include surrounding context, not just the error line]

What I have already tried:
1. [ATTEMPT 1 AND RESULT]
2. [ATTEMPT 2 AND RESULT]

Environment:
- [LANGUAGE/FRAMEWORK VERSION]
- [OS]

Task: Identify the root cause of the error and propose a fix. Explain your reasoning in 3-5 sentences, then provide the corrected code.

Including what you have already tried prevents the model from suggesting fixes you have already ruled out. This pattern is especially useful for runtime errors where the stack trace alone is insufficient.

Pattern 3: Code Review with Specific Criteria

Generic "review this code" prompts produce generic feedback. Specifying review criteria yields actionable results.

Role: You are a code reviewer for a [LANGUAGE] codebase.

Code to review:
[CODE SNIPPET]

Review criteria (prioritized):
1. Security: Check for injection vulnerabilities, unsafe deserialization, or hardcoded secrets.
2. Performance: Identify O(n^2) or worse operations, unnecessary allocations, or N+1 query patterns.
3. Readability: Flag unclear naming, missing edge case handling, or overly complex logic.
4. Test coverage: Note untested branches or missing edge case tests.

Output format: A numbered list of findings. For each finding: severity (Critical/Warning/Info), location (line number or function name), description, and suggested fix.

This pattern scales well. You can adjust the criteria list for different review goals such as accessibility, API design, or database optimization.

Pattern 4: Documentation Generation from Code

Generating documentation from code is one of the highest-ROI uses of LLMs for developers. The key is specifying the audience and format.

Role: You are a technical writer specializing in [LANGUAGE] documentation.

Source code:
[CODE SNIPPET OR FUNCTION]

Task: Generate documentation for this code.

Audience: [API consumers / Junior developers / Open-source contributors]
Format: [JSDoc/Docstring/RST/Markdown]
Include:
- Function purpose (1-2 sentences)
- Parameter descriptions with types
- Return value description with type
- 1-2 usage examples
- Notes on edge cases or limitations

Constraints: Do not document implementation details. Focus on the public interface contract.

Specifying the audience matters. Documentation for API consumers should focus on usage and contracts, while documentation for contributors should include implementation notes and architectural context.

Summary

These four patterns share a common structure: define the role, specify the task with context, set explicit constraints, and prescribe the output format. Adapting this structure to your specific development workflow will produce more consistent and useful AI-assisted output.

The templates above are starting points. Adjust the bracketed placeholders to match your stack, conventions, and team standards. The goal is not to memorize prompts but to develop a repeatable structure that you can apply across different tasks and models.

Curated by B. Kelley for SoloSpark. More structured prompt resources at solo-spark-vault.base44.app.