Guides · Updated 2026-10-23

How to review code with AI for free: workflows that stick

Knowing that AI code review exists is one thing; having a workflow where every risky change actually gets reviewed — automatically, for free, without anyone remembering to do it — is another. This is the workflow guide: four concrete routines, from a 30-second manual check to a pre-commit hook that reviews every staged file, all built on the free DoAide Code review tool and its keyless API.

(For what AI review catches and how it compares to human review, start with the companion guide: how AI code review works. This one assumes you're sold and want the habit.)

Workflow 1: the 30-second pre-commit check (manual)

You've just finished a function or fixed a bug. Copy the changed function — not the whole file; focused input gets sharper findings.
Open code.doaide.com/tools/code-review (bookmark it; the page remembers recent snippets on your machine).
Paste, press Ctrl/⌘ + Enter.
Triage the findings top-down: correctness and security first — fix those before anything else. Performance next if the code is on a hot path. Style last, and feel free to disagree with it.
Fix, re-run. A clean (or consciously-accepted) second pass is your commit signal.

That's the whole habit. The trick to making it stick: attach it to a trigger you already have, like writing the commit message — "no commit message before the review verdict".

Workflow 2: reviewing a pull request (yours or a teammate’s)

For a PR, review the diff, not the files:

Get the changed code: git diff main...feature-branch, or copy the changed functions from the PR's Files view.
Review each substantial changed function separately in the review tool — three focused reviews beat one giant paste.
For a change you don't understand, run Explain code first, then review — you'll judge the findings better.
Check the diff itself with the diff checker for a clean side-by-side when the platform's view is noisy.
If the PR adds logic without tests, paste the new function into Generate tests and attach the suite to your review comment. Reviewers who bring tests get listened to.

Reviewing your own PR this way before requesting human review is the single best "make reviewers like you" move: your PRs arrive pre-cleaned, and humans spend their attention on design.

Workflow 3: automate it — AI review in a git pre-commit hook

The review you don't have to remember is the one that always happens. The same review tool is a free REST API — one POST, no key — so a git hook can review every staged file:

#!/bin/sh
# .git/hooks/pre-commit — AI-review staged files (advisory, never blocks)
git diff --cached --name-only --diff-filter=ACM | while read -r f; do
  jq -n --arg code "$(git show ":$f")" '{action:"review", code:$code}' \
  | curl -s https://code.doaide.com/api/public/code-tool \
      -H 'Content-Type: application/json' -d @- \
  | jq -r '.answer' | sed "s|^|[$f] |"
done
exit 0

Two design choices worth copying: it's advisory (always exits 0 — a busy API should never block your commit), and it reviews staged files, not the whole repo, staying well inside the fair-use limits (8 requests per 10 minutes). For CI, the same call works in any pipeline step; treat failures as warnings there too.

Workflow 4: reading AI findings like a senior engineer

The skill that multiplies all three workflows is triage. For each finding, ask:

Is it real? Reproduce correctness findings before fixing: write the failing input as a test (the bug finder hands you the input). A finding you reproduced is a bug; one you can't is a question.
Is it in scope? A valid finding about code you didn't touch goes in a ticket, not in this commit. Keep diffs focused.
Is it a pattern? The third time review flags your missing error handling, the finding isn't about this function — it's about your habits. That's the free mentorship hiding in the tool.
Do you disagree? Fine — but say why in a comment or commit message. "Reviewed, accepted the O(n²) because n ≤ 20" is engineering; silently ignoring findings is how they stop being read.

The complete free review stack, assembled

Putting it together with the rest of the free layer (full comparison in best free code review tools):

Editor: linter + formatter on save — catches the mechanical issues before AI ever sees them.
On commit: the pre-commit hook above, or the 30-second manual check for risky changes.
On PR: workflow 2 — self-review, tests attached.
In CI: lint + tests as gates; AI review as an advisory step.
Humans: design and requirements — the machines did the rest.

Total cost: ₹0. Total setup: one bookmark and one shell script.

Review your code free — no sign-up

Frequently asked questions

How do I review code with AI for free?

Paste the function or file into a free review tool like code.doaide.com/tools/code-review — no account needed — and triage the findings: correctness and security first, then performance, then style. For automatic review, call the free API from a git pre-commit hook.

Can I automate AI code review on every commit?

Yes. The DoAide Code review tool is also a keyless REST API; a ten-line pre-commit hook reviews every staged file and prints findings before you commit. Keep it advisory (never blocking) and within the fair-use limit of 8 requests per 10 minutes.

Should I review the whole file or just my changes?

Review the changed functions plus just enough surrounding code for context. Focused input produces sharper findings, uses fewer requests, and keeps your fixes inside the scope of the commit.

What should I do when the AI review is wrong?

Treat findings as claims, not verdicts: reproduce correctness findings before fixing, and when you disagree, record why. AI review is a rigorous first pass — the judgment stays yours.

Can AI review a whole pull request at once?

The free paste-in tool reviews what you paste, so review a PR as its changed functions, one at a time. For changes spanning a whole repository, DoAide Code’s local assistant works on your clone directly.