10 senior engineer prompts for safer AI coding
Ten copy-ready prompts that turn an AI coding session into reviewed, tested and recoverable engineering work.
AI can generate a feature in hours. Senior engineering habits decide whether that feature survives contact with real users. The difference is not typing speed. It is the set of gates between “the code exists” and “the change is safe to ship.”
Each habit below is turned into a prompt you can paste into an AI coding tool. Fill in the brackets, keep the parts that match your project, and make the agent show its work before it changes anything risky.
1. Never ship code you have not read
Generated code is still your code. Before you approve it, make the agent translate the diff into plain language and point at the places where its assumptions could be wrong.
Walk me through every file changed for [task].
For each file, explain:
1. what changed,
2. why it was necessary,
3. what behavior could break,
4. the riskiest line or assumption.
Do not make more changes yet. End with the exact checks I should run.
2. Never put a real secret in browser code
A browser bundle is public. But not every key-shaped value is a secret: some public client identifiers are designed to be exposed. The important skill is classifying the value correctly, then rotating anything that was genuinely leaked.
Scan this project for credentials and configuration related to [service].
Classify each finding as:
- secret and server-only,
- intentionally public client configuration,
- uncertain and needing documentation.
Do not print any credential value. Report only its variable name and location.
If a secret may have reached source control or a browser bundle, stop and give me a
rotation and cleanup plan. Do not rewrite Git history or apply changes until I approve.
3. Never improvise a data model while building the screen
For a data-backed feature, decide what the system stores before asking the agent to wire buttons and forms. Otherwise the first convenient UI shape quietly becomes the database design.
Before building [feature], propose the smallest data model that supports it.
Show:
- each table or stored object,
- every field and type,
- required versus optional values,
- relationships and deletion behavior,
- ownership and authorization rules,
- one realistic example record.
Call out assumptions and wait for confirmation before writing migrations or app code.
4. Never let the stack drift silently
Letting an agent add a package whenever it sees a familiar problem creates dependency sprawl: more code to update, audit and understand. Record the approved stack and make any exception a visible decision.
Use this approved stack for [project]:
[framework, database, auth, hosting, validation and test tools].
Prefer existing project utilities and platform features. Before adding a package,
service or architectural pattern outside this list, explain:
1. the problem it solves,
2. why the current stack cannot solve it cleanly,
3. its maintenance and security cost.
Wait for approval before installing or switching anything.
5. Never build twelve features before one works end to end
Large batches hide where the design first went wrong. A thin, complete slice gives you usable evidence early: one path through the data, logic, interface and checks.
Build only this feature: [one feature].
It is done when a user can [one observable action] and these checks pass:
[acceptance checks].
List anything tempting but outside this scope. Do not implement those items.
Finish and verify this slice before proposing the next one.
6. Never say “fix it” without preserving the evidence
The full error and the steps that produced it are diagnostic evidence. If you skip them, the agent starts guessing, and each guess can bury the original cause under a new failure.
Here is the full error:
[paste the complete error]
I produced it by:
[exact steps]
First reproduce it and explain what the failing line means in plain language. Rank
the three most likely root causes and name the evidence for each. Test the strongest
hypothesis first. Then make the smallest coherent fix for the proven cause and add a
regression check. Do not change unrelated files.
7. Never test only the happy path
The happy path proves that ideal input works. Production failures live in missing permissions, empty values, retries, timeouts and double-clicks.
For [feature], list the important failure paths before changing code.
Include invalid input, empty state, unavailable dependency, permission denial,
duplicate action, slow response and interrupted request where relevant.
For each path, inspect the current behavior and state the expected safe behavior.
Then add or update the smallest set of tests that proves both the happy path and the
highest-risk failures. Never expose raw internal errors or sensitive values.
8. Never use one giant weekly commit
A commit is a recoverable checkpoint, not a ceremonial backup. Small, coherent commits make review, rollback and debugging much easier.
When [task] is complete, show me the diff summary and run [tests and checks].
If they pass, propose one commit containing only this coherent change. Show the exact
files and commit message first. Do not include unrelated working-tree changes, and do
not commit until I approve.
9. Never make a risky deployment when nobody can respond
“Never deploy on Friday” is a useful warning, not a law. The real rule is to match deployment risk with observability, rollback speed and human coverage.
Prepare [release] for deployment, but do not deploy yet.
Verify:
- the production build and relevant tests,
- required environment variable names without printing values,
- database migrations and compatibility,
- monitoring or logs for the changed behavior,
- the rollback procedure and its trigger.
State the expected blast radius and who needs to be available after release. End with
a clear go or no-go recommendation and wait for explicit approval.
10. Never accept “it works” without evidence
An agent saying a command passed is a claim. The command output, rendered behavior and absence of new warnings are evidence.
Prove that [feature or fix] works.
Run the relevant typecheck, tests, lint and production build. Exercise the real user
flow at [target viewport or environment]. Inspect the browser console and application
logs for new warnings or errors.
Report each command, its result and any check you could not run. Do not describe the
work as complete if a required check is skipped or failing.
Put the durable rules where the agent will see them
Do not paste all ten blocks into every conversation. Conversation prompts are for the task in front of you. Stable rules belong in the project instruction file your coding agent reads at the start of each session.
For this repository:
- stay inside the requested scope,
- explain and request approval before adding dependencies,
- never print, move or expose secret values,
- preserve unrelated working-tree changes,
- show risky or destructive actions before running them,
- read the diff and run the relevant checks before claiming completion,
- report skipped or failing checks honestly.
That file is not magic. It is a version-controlled agreement about how work should be done. Keep it short enough that the important rules remain visible, and revise it when the project teaches you a better rule.
Common questions
- Should I paste all ten prompts at once?
- No. Use the prompt that matches the decision or risk in front of you. Put only the durable rules in project instructions, because a giant permanent prompt wastes context and makes the important rule harder to notice.
- Do better prompts make AI-generated code safe?
- No. A prompt can improve the process, but it cannot prove the result. You still need to read the change, run the relevant checks and keep sensitive or destructive actions behind explicit approval.
- Where should permanent AI coding rules live?
- Use the project instruction file your tool reads, such as AGENTS.md, CLAUDE.md or Cursor project rules. Keep it short, specific to the repository and under version control so changes are reviewable.
- Do these prompts replace code review?
- No. They make the work easier to review by forcing the agent to expose assumptions, scope and evidence. The reviewer still decides whether the change is correct and safe to ship.
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