The verdict, task by task
Run down this list before you ask it anything. The pattern is consistent: it's strong where the answer is a judgement about language and weak where the answer is a fact held in someone's database.
| Task | Verdict | Why |
|---|---|---|
| Grouping a keyword list by intent | Pass | Pure language judgement. Faster and often better than doing it by hand. |
| Writing a content brief from a target query | Pass | Structure, entities to cover, questions to answer. Still needs a human to check against the live results. |
| Drafting titles and meta descriptions | Pass, with counting caveats | Good at angles, unreliable at character limits. Count them yourself. |
| Generating schema markup | Pass | Produces valid JSON-LD quickly. Validate it, and check the page actually supports the claims. |
| Explaining a technical concept or debugging regex | Pass | Well-documented territory. This is what it's best at. |
| First drafts of routine copy | Pass, edited | Category text, FAQ answers, product blurbs. Nothing ships unedited. |
| Monthly search volume for a keyword | Fail | No access to the underlying data. It produces a plausible number, not a measured one. |
| Keyword difficulty scores | Fail | These are vendor-specific formulas over live link data. It cannot compute them and will still give you one. |
| Backlink counts or referring domains | Fail | Requires a live link index. Numbers here are invented. |
| "Who currently ranks for X" | Fail without browsing | Rankings are personalised, local and change daily. Without a live fetch it's guessing from training data. |
| Deciding what to publish next | Fail | Depends on your margins, your sales cycle and what your team can actually ship. It doesn't know any of that. |
Why it can't give you search volume, ever
This is the failure people get burned by most, so it's worth understanding the mechanism rather than just the rule.
Search volume isn't a fact sitting on the open web. Google doesn't publish it. The numbers in every SEO tool come from two places: Google's own advertiser-facing data, which is bucketed and rounded and only visible through an ads account, and third-party clickstream panels that vendors buy and model on top of. Both are licensed, commercial datasets.
A language model has read none of that. When you ask for the volume of "seo agency in pune", it generates the most statistically plausible-looking number in context — a round figure, a believable order of magnitude, delivered in the same confident tone as everything else it says. It isn't lying. It's completing a pattern. The output is indistinguishable in tone from a real figure, which is exactly what makes it dangerous.
The fix is a tool connection, not a better prompt. When a model is wired to a real API — Search Console, an ads account, a rank tracker — it stops guessing because it's reading. If it's only reading its own weights, no amount of "be accurate" changes the output.
Where it genuinely earns its keep
Three uses justify the subscription on their own, and each works better with a specific prompt shape.
Clustering
Paste an exported keyword list — real volumes attached, from a real tool — and ask it to group by search intent and by the page that should serve each group. This is the single best use of a model in SEO work, because it's a language problem at a scale humans handle badly.
- Works: *"Group these 400 queries into clusters that could each be served by one page. Label each cluster with the intent and the page type. Flag any that belong on a page we already have."*
- Misleads: *"Find me the best keywords for my business."* No data, no context, confident answer.
Briefs
Give it the target query, the live top five URLs, and your own constraints. Ask for structure, entities, questions to answer and what would make your version different. Then check the brief against the actual results page yourself, because its idea of what the query means is a prior, not an observation.
- Works: *"Here are the five pages currently ranking. Here's what we sell. Write a brief for a page that answers something these five don't."*
- Misleads: *"Write a 2,000-word SEO-optimised article on X."* You'll get a competent average of everything already published — which is the definition of a page nobody needs.
Titles, metas and internal link suggestions
Fast, good at angles, careless with limits. Ask for ten variants and pick, rather than asking for one and accepting it. For internal links, paste your actual URL list — it can't guess your site structure and will invent paths if you let it.
The check that goes before publish
Five checks, in order. Skipping the first two is how sites end up with confident nonsense on a money page.
- Every fact about your business. Prices, turnaround, guarantee terms, service areas, what you refuse to do. Models produce fluent sentences about policies you've never had.
- Every external fact and every citation. If it names a study, a percentage or a source, open it. Fabricated-but-plausible references are the classic failure, and a made-up statistic on your site is a credibility problem long before it's a ranking problem.
- Every number that came from the model rather than a tool. Volumes, difficulty, traffic estimates, competitor figures. Delete or replace.
- Intent, against the live results. Open the query in an incognito window. If the SERP is all product pages and your brief says "write a guide", the brief is wrong.
- The voice. Models default to hedged, symmetrical, list-heavy prose. If it reads like everything else on the topic, it is everything else on the topic, and it will be summarised rather than clicked.
What it costs against the paid tools
The honest comparison isn't ChatGPT versus Ahrefs. They do different jobs — one produces language, the other holds data — and most working setups pay for both. List prices move, so treat these as orders of magnitude and check before you budget.
| What you're paying for | Rough monthly cost | What it gets you |
|---|---|---|
| Google Search Console | ₹0 | Your own impressions, clicks, queries and index status. The most important dataset you have, and it's free. |
| A consumer AI subscription | Roughly ₹1,500–₹2,500 | Clustering, briefs, drafts, schema, explanations. Replaces junior hours, not data. |
| An entry seat on a major SEO suite | Roughly ₹9,000–₹15,000 | Real volumes, difficulty, live rank tracking, a link index. The things the model can't know. |
| A rank tracker on its own | Roughly ₹1,500–₹5,000 | Cheaper if you only need positions and already have Search Console for the rest. |
A division of labour that holds up
Give the model the language work and keep the data and the decisions. In practice that means: it clusters, you validate. It briefs, you check the SERP. It drafts, you add the parts only you know — your prices, your test results, the client project that went sideways. It suggests, you decide what not to publish.
That last one matters more than it sounds. Models are biased towards producing more, and more is precisely what stopped working. Google's spam policies name scaled content abuse directly, and the risk sits in publishing volume nobody would miss rather than in the tool that produced it — the full picture is in does AI content hurt SEO.
Used this way, ChatGPT genuinely removes the tedious half of SEO work. It does not remove the need to know what you're looking at, which is the half that was always expensive.