A complete AI-assisted review pass — not one prompt — that ends with ranked findings, tests guarding behavior, and a refactor plan when one is warranted.
One review prompt reads a diff and hands back an opinion. A real review is more than that. Give the model no project context and it invents conventions you don't follow. Let it judge code it doesn't actually understand and it approves the wrong things confidently. And a review that lists problems but leaves behind no tests and no plan hasn't made the merge any safer. This workflow chains the coding tools you already have so each step sets up the next — context, then review, then understanding, then a safety net.
Each step uses an existing NewPrompt tool, pre-filled by a matching resource. Open the resource to read it, or jump straight into the tool with the inputs ready.
Prepare project context
Before the model reviews anything, give it the project's stack, conventions, and domain so it reviews against your standards instead of generic best practice. Build a context profile once and reuse it as the first message in every review session.
Generate the code review prompt
With context in place, generate the review contract — what to check (correctness, security, tests, design), how to rank findings (must-fix vs nit), and what format the output should take. Paste the diff after it.
Understand risky or unclear code
When the review flags code you don't fully understand — a dense function, legacy logic, an unfamiliar pattern — don't approve on faith. Get a grounded, line-aware explanation of what it actually does before judging the change.
Generate regression tests
For behavior the change touches, pin the current behavior with regression tests before anything moves. This turns 'looks fine' into a safety net that catches the bug the next change would reintroduce.
Plan a safe refactor if needed
If the review surfaces code that should be restructured, plan the refactor with zero-regression discipline — behavior preserved, changes sequenced, validation defined — rather than rewriting in place during the review.
You finish with severity-ranked findings, a real understanding of the code you were unsure about, regression tests guarding current behavior, and a behavior-preserving refactor plan when one is warranted. The change is genuinely safer to merge — not just looked at.
A single code review prompt reviews one diff and hands back an opinion. This workflow wraps five steps around it: set project context first, generate the ranked review, explain risky code, pin behavior with regression tests, then plan a safe refactor. Use the prompt for a quick look; use the workflow when the change matters.
Run this code review workflow to catch problems before a change merges. Run the AI Debugging Workflow once something is already broken and you need the cause. Run the AI Refactoring Workflow when restructuring is the actual task — step 5 here only sketches a refactor when the review surfaces one.
You finish with severity-ranked findings from the review, a grounded explanation of any code you were unsure about, regression tests that lock in current behavior, and a behavior-preserving refactor plan when the review warrants one. The deliverables make the change safer to merge — you still run each prompt and own the verdict.
You need the change to review — a diff or pull request — plus your project's stack, conventions, and domain so step 1 can build a reusable context profile. Steps 3 to 5 also draw on the specific risky sections, touched behavior, and code the review flags for restructuring. No account or setup is required.
No. Steps 1 and 2 are the core. Step 3 only applies when the review flags code you don't fully understand, and steps 4–5 apply when the change touches behavior or needs restructuring. Skip what a given review doesn't need.
No. Every tool in this workflow runs entirely in your browser and only generates prompts — you paste those prompts and your code into the AI you already use. NewPrompt never executes code or sends it to a server.
Complete build journeys that include this workflow as a stage.
The full path from idea to a shipped SaaS MVP — define and scope the requirements, design the architecture, API, and data model, then build it reviewed, tested, secured, cost-controlled, and deployed.
The full path to a backend you can put clients on — define the requirements, design the architecture, API contract, data model, and access control, then build it reviewed, tested, secured, and shipped.
The full path to taming an inherited codebase — understand it, document its architecture, pin its behavior with tests, then refactor, modernize, review, speed up, and ship it without breaking what works.
AI-written code usually compiles and passes a quick test while hiding the real risks — a missing auth check, a destructive update, an untested edge case. Here's a review path that surfaces them before you merge.
Build a test suite that fails for real reasons, not green decoration — coverage across unit, integration, and edge cases, then a review for the gaps.
Review code for what an attacker would do, not just what tests catch — anchor the model as a security engineer, run a threat-focused review, then back the findings with auth and input tests.
The order that actually finds bugs instead of guessing at them — so you end with a verified fix, not a plausible one that quietly returns next week.
Restructure code you own without breaking it — change only what's worth changing, and prove with tests and a diff that behavior held.