Custom GPT as a QC Code Reviewer

What This Builds

A standing ChatGPT configuration, built once, that reviews R, SAS, or Python code against your team's own coding standards and QC checklist every time you paste in a script. Instead of writing out the same review instructions in a fresh chat each time, or hoping a generic prompt happens to catch the same issues your team's checklist would, this GPT already knows the checklist because you uploaded it once when you built it.

Prerequisites

• Plus subscription ($20/month). Building and using a Custom GPT requires a paid ChatGPT plan, not the free tier.
• A written coding standards document or style guide for your team, even an informal one
• A QC checklist or list of common findings from past code reviews, de-identified with no study-specific or client-specific detail

The Concept

A Custom GPT is a version of ChatGPT with a standing set of instructions and reference documents attached, so you don't have to explain your team's rules from scratch every time. It's closer to handing a new reviewer your team's style guide and QC checklist once, on their first day, rather than re-explaining what counts as a problem every single time you ask them to look at a script. The GPT reads whatever code you paste against those documents and flags what doesn't match, before a person spends time doing the same check by hand.

This is a first pass, not a replacement for your team's independent programming or code review step. It catches checklist-level issues fast. It does not carry the authority, or the judgment, of the human QC process it feeds into.

Build It Step by Step

Part 1: Set up the GPT and its instructions

From ChatGPT, open the GPT builder. Look for "Explore GPTs" or "My GPTs" in the sidebar. Select "Create." Give it a name your team will recognize, like "QC First Pass" or "[Team Name] Code Reviewer." In the Instructions field, paste something close to this, then adjust it to match your team's actual standards:

You are a first-pass QC reviewer for statistical programming code (R, SAS, or
Python) written for a corporate or clinical statistics team. You are reviewing
CODE and LOG OUTPUT only. You will never receive real data, and if a user pastes
anything that looks like row-level subject, claims, or survey data, stop and ask
them to remove it before continuing.

When reviewing code, check for:
- Unhandled missing values (functions that assume complete data)
- Hard-coded values that should be parameters (sample sizes, cutoffs, file paths)
- Mismatched variable labels or inconsistent naming
- Logic that contradicts the stated analysis method or population definition
- Anything matching a finding in the attached QC checklist document

For each issue found, quote the specific line or block, name which rule it
violates, and suggest a fix. If the code looks clean against the checklist, say
so explicitly rather than inventing an issue. End every review by reminding the
user this is a first pass, not a substitute for the team's independent
programming or code review step.

Part 2: Attach the knowledge files

In the GPT builder's Knowledge section, upload your coding standards document and your QC checklist. Keep both files free of real study names, client names, or data. If your checklist currently lives in someone's head or an old email thread, this is the moment to write it down properly. The GPT can only check against what's actually in the file.

Part 3: Test it and fix the gaps

Paste a script with two issues you planted yourself (a hard-coded sample size and a missing-value check that's absent) and see whether the GPT catches both. If it misses one, check whether that finding is actually written into your checklist document. A gap in the GPT's output is usually a gap in the uploaded document, not a failure of the model.

Real Example: Reviewing an R Function Before Human QC

Setup: A statistical programmer built a GPT named "QC First Pass" with the team's coding standards and a checklist covering the eight most common findings from the last year of code reviews.

Input: Before sending a finished R function to the human QC reviewer, the programmer pastes the full function and the log output from running it on test data into the GPT, with no real data included.

Output: The GPT flags that the function assumes a nonzero input length without checking for an empty vector, a finding that matches item 3 on the uploaded checklist, and quotes the exact line. It also confirms the variable labels match the data dictionary convention, so that item passes.

Time saved: Catching the empty-vector issue here means the human reviewer's time goes toward genuinely new problems, not the checklist item the GPT already caught. Across a week of reviews, this trims the obvious, mechanical portion of the QC hours the team spends, without shortening the actual independent review.

What to Do When It Breaks

• The GPT invents a rule that isn't actually in your standards document → Ask it to quote the exact line of the uploaded document it's citing. If it can't, treat that finding as a suggestion, not a rule violation.
• A knowledge file update doesn't seem to change its answers → Re-upload the file rather than assuming an edit to the original document synced automatically. Custom GPT knowledge files are snapshots, not live links.
• Someone pastes a log containing what looks like real subject or claims data → The instructions above tell the GPT to stop and flag this, but treat that as a backstop, not the actual control. Confirm with whoever owns your data policy which review tools are approved for code and logs before your team adopts this GPT.

Variations

• Simpler version: Skip the knowledge file uploads at first and put a short version of the checklist directly in the Instructions field, good for a team of one testing the idea before writing a full standards document.
• Extended version: Build a second GPT tuned specifically for SAS macro review, since SAS's log warnings and notes follow different conventions than an R or Python traceback.

What to Do Next

• This week: Run your last three reviewed scripts through the GPT retroactively and compare its findings against what the human reviewer actually caught, to see where the checklist has gaps.
• This month: Add any new finding categories from that comparison back into the checklist document. Re-upload it so the GPT can see the change.
• Advanced: Connect this GPT's review step to the scheduled macro documentation workflow, so newly documented macros get a QC first pass at the same time they get documented.

Advanced guide for statistician professionals. These techniques use more sophisticated AI features that may require paid subscriptions.