Best ChatGPT & Claude Prompts for Developers (Copy-Paste Templates)
Generic 'write me some code' prompts produce generic results. These templates are structured around the specific failure mode each one prevents — copy, adapt the bracketed parts, and use.
A prompt template is only useful if you understand what it's actually preventing — otherwise it's just words to paste. Each template below targets one specific way generic prompting goes wrong. Copy the structure, fill in the brackets with your actual context, and keep the constraints even when they feel unnecessary — they're doing the real work.
1. Code review with a specific lens
"Review this code" produces generic, hedge-y feedback. Naming the lens gets specific, actionable findings.
Review the following code for [ERROR HANDLING / SECURITY / PERFORMANCE — pick one] issues only.
[paste your code]
For each issue found: cite the exact line, describe the specific scenario where it causes a problem, and suggest a concrete fix. Do not comment on style or naming unless it directly causes a bug.2. Explaining unfamiliar code
I'm new to this codebase. Explain what the following code does.
[paste code]
Structure your answer as:
1. One-sentence summary of its purpose.
2. Step-by-step walkthrough of the logic.
3. Any edge cases or error conditions it does NOT appear to handle.
4. One question I should ask a teammate if I wanted to confirm my understanding.3. Debugging with evidence, not guesses
The single biggest lever in debugging prompts: paste the actual error/stack trace/logs, not a description of them. "It's throwing a null reference somewhere in the checkout flow" gives the model almost nothing to work with; the actual stack trace gives it something to reason about.
Here is the full error/stack trace: [paste exact error text, unedited]
Here is the relevant code: [paste the code the trace points to, plus its immediate callers]
What I was doing when this happened: [one sentence]
Based only on the evidence above — not general guesses about what 'usually' causes this error — identify the most likely root cause and the minimal fix. If you need more information to be confident, say exactly what additional evidence would confirm it, rather than guessing.4. Generating tests that actually test behavior
Here is a function and its intended behavior (not just its current code): [paste function + a one-paragraph description of what it should do, including edge cases]
Write unit tests using [your framework, e.g. Jest / xUnit / pytest] that verify this intended behavior, including:
- The normal/happy-path case.
- At least 2 edge cases (empty input, boundary values, etc.).
- At least 1 error/invalid-input case.
Do not write a test that would pass against ANY implementation regardless of correctness — every test must be capable of failing if the logic is wrong.5. Preventing hallucinated APIs
Context: [paste the real API/library signatures or an existing working example using this library]
Task: [what you want built, using the library above]
Constraint: Only use methods, types, and imports that appear in the context above, or that you are certain exist. If you're not certain a method exists, say so explicitly rather than guessing.6. Getting the model to check its own work
Here is code you (or I) just wrote: [paste code]
Review it as if you were a skeptical senior engineer seeing it for the first time. Specifically check for: unhandled edge cases, assumptions that might not hold, and anything that would fail under concurrent access or unexpected input. List concrete issues only — do not say 'looks good' unless you've actually checked each of these categories.Every template above closes off a way the model could give you something plausible-sounding but wrong: a specific lens instead of vague review, real evidence instead of a description, an explicit "say so if uncertain" instead of silent guessing. That's the actual pattern behind good prompting, not the specific wording.
These six cover the most common day-to-day developer tasks. The full Prompt Library has 40+ named prompts going deeper into specific patterns — including self-critique loops, negative-constraint templates, and architecture reverse-engineering, each with the reasoning behind why it's structured the way it is, not just the template text.