Prompt engineering is the practice of designing inputs that reliably guide large language models to produce accurate, structured, and useful outputs.
For developers, prompt engineering is not about clever wording.
It is about controlling ambiguity, constraining solutions, and shaping reasoning so the model produces correct code.
This guide covers:
- the core prompt engineering techniques
- why they exist
- when to use each
- how they apply to real coding workflows
What Is Prompt Engineering?
Prompt engineering is the process of turning an intent into a precise instruction that a model can execute correctly.
In software development, this includes:
- generating code
- refactoring safely
- debugging errors
- enforcing style and structure
- producing machine-readable outputs
You can think of prompts as specifications.
Poor specifications produce poor implementations.
Core Prompt Engineering Techniques
Below are the standard techniques used in prompt engineering, explained in a practical, developer-friendly way.
1. Zero-Shot Prompting
What it is:
Asking the model to perform a task without giving examples.
Example:
Write a function that removes duplicate values from an array while preserving order.
When to use it:
- Common tasks
- Well-defined problems
- Straightforward code generation
Limitations:
Fails when format, style, or edge cases matter.
2. Few-Shot Prompting
What it is:
Providing one or more examples to demonstrate the desired pattern.
Example:
Convert text to snake_case.
Input: "User Name"
Output: "user_name"
Input: "Account ID"
Output: "account_id"
Now convert: "Order Number"
When to use it:
- Output formatting matters
- Naming conventions
- Transformations and parsing
- UI or data normalization
Few-shot prompting dramatically improves consistency.
3. Instruction Prompting
What it is:
Explicitly telling the model what to do.
Example:
Refactor this code for readability.
Do not change behavior.
Remove dead code.
Why it matters:
Models follow instructions better than vague descriptions.
4. Contextual Prompting
What it is:
Providing background information that shapes decisions.
Example:
This code is part of a TypeScript backend.
The project enforces immutability and strict typing.
Refactor the function accordingly.
When to use it:
- Existing codebases
- Architectural constraints
- Style enforcement
Context reduces incorrect assumptions.
5. Role Prompting
What it is:
Assigning the model a role to bias decision-making.
Example:
Act as a senior software engineer.
Review the following code and suggest improvements.
Why it works:
Different roles imply different trade-offs (performance, safety, readability).
6. Chain-of-Thought Prompting
What it is:
Asking the model to reason step-by-step before answering.
Example:
Analyze the problem step-by-step.
Explain the root cause.
Then provide the corrected code.
Best used for:
- Debugging
- Algorithms
- Complex logic
- Data processing
This improves correctness and transparency.
7. Delimiter Prompting
What it is:
Separating instructions from code or data.
Example:
Analyze the following code and identify bugs:
[code]
Why it matters:
Prevents the model from mixing instructions with content.
8. Structured Output Prompting
What it is:
Explicitly defining the output format.
Example:
Return the result in this format:
{
"summary": "...",
"issues": [],
"fixed_code": "..."
}
Essential for:
- Automation
- Tooling
- Programmatic consumption
9. Constraint Prompting
What it is:
Defining hard rules the solution must follow.
Example:
Rewrite this function with these constraints:
- No external libraries
- Time complexity must be O(n)
- Do not change the public API
Constraints dramatically reduce bad solutions.
10. Error-Driven Prompting
What it is:
Using real error messages as input.
Example:
Here is the exact error and stack trace:
[error]
Explain the root cause and provide a fix.
Why it works:
Error messages anchor the model to reality.
11. Self-Reflection Prompting
What it is:
Asking the model to review and correct itself.
Example:
Review your previous answer.
Identify edge cases and improve the solution.
This often catches subtle bugs.
12. Prompt Chaining
What it is:
Breaking a complex task into multiple prompts.
Example workflow:
- Analyze requirements
- Define interfaces
- Implement logic
- Add validation
- Write tests
Each step has a focused prompt.
Coding-Focused Use Cases
Code Generation
Write a reusable debounce utility.
Handle edge cases and explain the logic.
Refactoring
Refactor this code for readability without changing behavior.
Debugging
Explain why this function fails for negative values and fix it.
Performance Optimization
Analyze time and space complexity.
Optimize the function and explain the changes.
Documentation
Generate documentation with usage examples for this module.
Best Practices
- Be explicit
- Provide context when needed
- Use examples when format matters
- Define output structure
- Add constraints early
- Use step-by-step reasoning for complex tasks
- Break large problems into smaller prompts
Prompt engineering is iterative, like coding.
Prompt Engineering Cheat Sheet
Techniques
| Technique | When to Use |
|---|---|
| Zero-shot | Simple, common tasks |
| Few-shot | Formatting, transformations |
| Instruction prompting | Direct actions |
| Contextual prompting | Codebase consistency |
| Role prompting | Quality and trade-offs |
| Chain-of-thought | Logic and debugging |
| Structured output | Automation |
| Constraint prompting | Safety and correctness |
| Error-driven prompting | Debugging |
| Prompt chaining | Large workflows |
Common Coding Tasks
| Task | Example Prompt |
|---|---|
| Generate code | “Write a function that…” |
| Refactor | “Improve readability without changing behavior.” |
| Debug | “Explain and fix this error.” |
| Optimize | “Reduce time complexity.” |
| Document | “Add documentation with examples.” |
| Review | “Review and suggest improvements.” |
Final Thought
Prompt engineering is not about talking to AI.
It is about designing instructions.
The better your prompts, the more predictable and useful the output becomes.
Treat prompts like code:
- structured
- constrained
- reviewed
- improved over time
That’s how AI becomes a real development tool.
Top comments (0)