TIPS
5 Prompt Patterns Every Developer Should Know
5 min read
Prompt patterns are reusable structures that consistently produce better AI output. Just like design patterns in software engineering, knowing these patterns gives you a toolkit for solving common problems. Here are five patterns every developer should have in their arsenal.
1. Chain of Thought
Chain of Thought (CoT) prompting asks the model to show its reasoning step by step before arriving at a final answer. This is especially powerful for debugging, logic problems, and code review tasks where the reasoning process matters as much as the result.
Example
“This function is returning incorrect results. Walk through the logic step by step, checking each condition and variable state. Then identify the bug and suggest a fix.”
By asking the model to think aloud, you reduce the chance of it jumping to wrong conclusions. The intermediate steps also make it easier for you to verify the reasoning and catch mistakes early.
2. Few-Shot Examples
Few-shot prompting gives the model two or three examples of the input-output pairs you expect. The model recognizes the pattern and applies it to new inputs. This is invaluable for code transformations, data formatting, and generating consistent output.
Example
“Convert these API responses to TypeScript interfaces:
Input: { "name": "John", "age": 30 }
Output: interface User { name: string; age: number; }
Input: { "title": "Hello", "published": true }
Output: interface Post { title: string; published: boolean; }
Now convert this: { "price": 9.99, "items": ["a","b"], "active": false }”
The more consistent your examples, the more consistent the output. Aim for two to three examples that cover different edge cases.
3. Role Assignment
Assigning a specific role or persona to the AI changes how it approaches a problem. A prompt that starts with “You are a senior backend engineer with 10 years of experience in distributed systems” will produce different (and often better) output than a generic request.
Role assignment works because it narrows the model's response space. Instead of drawing from its entire training data, it focuses on patterns associated with that expertise. Use this for code reviews (“You are a security auditor”), architecture decisions (“You are a solutions architect”), and technical writing (“You are a developer advocate writing for junior engineers”).
4. Structured Output
Telling the model exactly how to format its response saves time and makes outputs directly usable. Instead of getting a paragraph you need to parse, ask for JSON, markdown tables, numbered lists, or specific code structures.
Example
“Analyze this error log and return the results as a JSON object with these fields: error_type (string), root_cause (string), severity (high/medium/low), and suggested_fix (string).”
This pattern is essential when you are piping AI output into other tools or scripts. Combine it with a schema definition or example output for even more reliable formatting.
5. Iterative Refinement
Rather than trying to write the perfect prompt on the first attempt, use an iterative approach: start broad, evaluate the output, and refine. Each iteration tightens the constraints based on what the model got wrong or missed.
This is where prompt version control becomes essential. With PromptingBox, you can save each iteration, compare outputs side by side, and track what changes led to improvements. Over time, you build a library of battle-tested prompts that you know work.
A common workflow looks like this: write a v1 prompt, test it, identify gaps, add constraints or examples, test again, and repeat. After three or four iterations, you typically have a prompt that handles edge cases and produces reliable output.
Putting It All Together
These patterns are not mutually exclusive. A well-crafted prompt often combines role assignment with chain of thought and structured output. The key is to be intentional: choose the patterns that match your task, save what works, and iterate on the rest.
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