Three years into using ChatGPT alongside real ad accounts, the pattern is clear: it is a phenomenal analyst and a dangerous consultant. Here is the honest inventory — what earns its seat in a paid-media workflow, what fails, and the prompt patterns that separate the two.
I have managed $350M+ in paid media over fifteen years, and for the last three of those years ChatGPT has been open in a tab next to every ad account I touch. Not because it is magic — because parts of this job are pattern-matching over text at volume, and that is exactly what a language model is for.
But the discourse is still split between two camps that are both wrong: the "ChatGPT replaced my agency" crowd and the "it's a toy that makes things up" crowd. The truth is boring and useful: ChatGPT is a phenomenal analyst when you hand it real data, and a dangerous consultant when you ask it open-ended questions about a platform that changes quarterly. This post is the inventory of what actually works, what fails, and the prompt patterns that make the difference.
Responsive Search Ads want 15 headlines and most humans run dry at eight. ChatGPT does not run dry. The trick is to feed it your last 90 days of converting search terms — not your product description — and ask it to write headlines in the language buyers actually used. When I do this for a lead-gen account, I paste 50–100 converting terms and ask for headlines grouped by intent theme, each under 30 characters, with the character count shown. It reliably produces 25–30 candidates of which 10–12 survive my edit. That is a 45-minute task compressed to ten.
This is the single highest-leverage use I have found. Export your search-terms report, paste it (or attach the CSV), and ask for a one-, two-, and three-word n-gram breakdown weighted by cost and conversions. Patterns invisible at the query level jump out at the phrase level: on one account, the bigram “near me” carried 22% of spend with a conversion rate 60% below account average — a single negative-keyword decision worth roughly $1,400/month. A spreadsheet jockey can build this in Excel; ChatGPT does it in one prompt and explains the findings in plain English while it's at it.
The same export, different question: “Group these terms into buckets: clearly irrelevant, competitor names, research-intent, purchase-intent. Draft a negative keyword list from the first bucket with suggested match types.” The bucketing is 90% right, which is the right way to think about it — it is a first-pass filter that turns a 2,000-row review into a 200-row review. You still make the calls; it just clears the underbrush.
Before I submit ads in regulated verticals — insurance, finance, health — I run copy through ChatGPT with the relevant policy pasted into the prompt: “Here is Google's personalized advertising policy on financial products. Flag anything in these ten ads that could trip disapproval, and explain why.” It will not catch everything, and it is not a substitute for the actual policy review, but it catches superlatives, implied guarantees, and missing disclaimers before Google does. Fewer disapproval loops means faster launches.
ChatGPT writes serviceable GAQL (the Google Ads Query Language) and Google Ads Scripts, with one giant caveat covered below. For well-trodden tasks — a script that pauses keywords over a CPA threshold, a GAQL query pulling asset-level PMax performance — it gets you 80% of the way in seconds. I always run its GAQL through the API's query validator and its scripts in preview mode first, because roughly one in four drafts references a field that doesn't exist or has been renamed. Still faster than starting from documentation.
The least glamorous, most consistent win. Paste the month's actuals — spend, conversions, CPA, the two or three things you changed — and ask for a client-facing narrative in your voice. What used to be the last painful hour of every reporting cycle is now a ten-minute edit. The key is that the numbers come from you; ChatGPT is only doing the prose.
Ask ChatGPT “what's a good CTR for legal services search campaigns?” and it will hand you a precise-sounding number — “typically 3.8–4.5%” — with complete confidence and zero source. These numbers are pattern-plausible, not measured. I have watched junior marketers put hallucinated benchmarks into client decks. If a number matters, it needs to come from your account, your vertical data, or a named published study — never from the model's memory.
Google Ads ships changes quarterly and deprecates features constantly. A model's training data is always months behind, and web browsing only helps if it actually browses — which it often skips for questions it “knows.” In 2026 I still see ChatGPT recommending modified broad match (dead since 2021), describing the old seven-day-click attribution defaults, and misstating how search themes work in Performance Max. Treat any platform-mechanics claim as unverified until you have checked the current documentation.
The most dangerous failure: invented capabilities described in fluent detail. I have seen it confidently explain a “PMax negative keyword tab” (account-level negatives shipped very differently), invent Google Ads Editor menu paths that do not exist, and cite a “quality score API endpoint” that was never public. It never says “I'm not sure this exists.” The fluency is the trap — wrong answers read exactly like right ones.
Once a prompt works, you face a fork: package it as a custom GPT or build it into a real automation via the API. They solve different problems.
| Custom GPT | API automation | |
|---|---|---|
| Best for | Repeatable human-in-the-loop tasks: copy review, n-gram runs, report drafts | Scheduled, high-volume, or multi-step work: weekly search-term triage, alerting, bulk copy generation |
| Build cost | An afternoon — instructions plus a few reference files | Days — code, hosting, error handling, monitoring |
| Consistency | Moderate; users can wander off-script | High; same prompt, same structure, every run |
| Data access | Whatever the user pastes or uploads | Direct pipes from Google Ads API, GA4, your warehouse |
| Failure mode | Someone trusts an unverified answer | A silent format drift breaks parsing downstream |
My rule: a task done weekly by a human belongs in a custom GPT with the policy docs and copy guidelines attached as knowledge files. A task done daily, or one that touches account data programmatically, belongs in an API automation with structured output and validation. Most teams over-invest in the second before exhausting the first — a well-built custom GPT for ad-copy review costs almost nothing and pays back immediately.
The difference between a useless and a valuable ChatGPT session in PPC is almost always the input. Three patterns:
One more that deserves its own paragraph: give it the role and the stakes. “You are reviewing ad copy for a licensed insurance agency; a disapproval delays launch by three days and a policy violation risks account suspension” produces meaningfully more careful output than “check this ad copy.” Models calibrate their thoroughness to the frame you give them. Marketers who write good creative briefs already know how to do this — a prompt is a brief, and the same discipline applies: context, constraints, audience, deliverable.
Spend any time in r/ChatGPT and the same business-workflow questions cycle weekly. The recurring ones, from a paid-media seat:
Everything in the “what works” list — n-grams, search-term triage, structure review — is what Buddy does natively, connected live to your Google Ads account instead of working from pasted exports. He pulls your real structure, scores it against practitioner frameworks, and returns a prioritized fix list with real numbers.
Open Buddy →If you take one thing from this: ChatGPT is a data-transformation tool wearing a consultant costume. Use the tool, ignore the costume. Concretely:
— John
