{"id":27413,"date":"2026-03-13T12:43:33","date_gmt":"2026-03-13T07:13:33","guid":{"rendered":"https:\/\/www.karooya.com\/blog\/?p=27413"},"modified":"2026-03-13T14:25:18","modified_gmt":"2026-03-13T08:55:18","slug":"chatgpt-the-full-ai-stack-for-negative-keyword-research","status":"publish","type":"post","link":"https:\/\/www.karooya.com\/blog\/chatgpt-the-full-ai-stack-for-negative-keyword-research\/","title":{"rendered":"Beyond ChatGPT: The Full AI Stack for Negative Keyword Research"},"content":{"rendered":"\n

Back in 2023, we wrote an article about using ChatGPT to identify negative keywords<\/a>. Cut to 2026, we thought that this topic needed to be revisited as a lot many changes have occured in the AI landscape that needs to be incorporated (if it is being put in practice and even if it is not, in near future you can expect reaching out to it for some mundane tasks)<\/p>\n\n\n\n\n\n\n\n

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Negative keywords has always acted as the backbone of a healthy paid search account. If you get it all wrong, then on one hand you bleed budget because of irrelevant impressions and on the other you choke your conversions, if you are too aggressive with your negative keyword strategy. As an advertiser it becomes a challenge to bridge the gap between too much and too less. <\/p>\n\n\n\n

AI tools made it a tad bit easier to help with the task of identifying negative keywords. But its role has evolved over the years, to now advertisers asking harder questions such as which model, what prompts, what guardrails, and how does this sit alongside automated platforms like Performance Max that already fight your negatives at every turn?<\/em><\/p>\n\n\n\n

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Why the 2023 Post Needed Updating?<\/strong><\/h2>\n\n\n\n

Majorly three things have changed since the 2023 written post:<\/p>\n\n\n\n

First, Google’s own automation has become far more aggressive. Match types have deteriorated<\/strong> significantly \u2014 as @Josephwilliams<\/a> put it in the December 2025 PPCChat session<\/a>: <\/p>\n\n\n\n

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“The overall deterioration of match types as a whole… 2025 has given me some of the craziest ‘matches’ I’ve seen in my short 7 years of doing this.”<\/em>.<\/p>\n<\/blockquote>\n\n\n\n

Performance Max now touches search inventory with limited transparency, which means negative keywords must work harder than ever.<\/p>\n\n\n\n

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Second, the AI model landscape diversified. ChatGPT no longer sits alone; Gemini, Claude, and several other models each have distinct strengths for this kind of structured research task. The choice of model matters more than many guides acknowledge.<\/p>\n\n\n\n

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Third, a new generation of purpose-built PPC campaign management tools has emerged that integrate AI-powered negative keyword recommendations directly into your workflow \u2014 with access to your actual account data \u2014 removing the copy-paste dance entirely.<\/p>\n\n\n\n

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Industry context<\/strong> In a December 2025 PPCChat session<\/a> recap published by us, practitioners described AI as the single biggest acceleration in PPC in 2025, while simultaneously noting that trust in platforms is at an all-time low. The two are connected: practitioners are using AI-assisted research to reclaim the control that Google’s match type and automation changes have stripped away. <\/p>\n<\/blockquote>\n\n\n\n

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Tactic 1: The Search Term Dump<\/strong><\/h3>\n\n\n\n


Instead of asking an AI to imagine what negatives you might need, feed it real data. Export 90 days of search terms from Google Ads (no conversion data required), paste it into Claude or GPT-4o, and use a structured prompt to identify patterns.<\/p>\n\n\n\n

Sample prompt \u2014 search term analysis<\/p>\n\n\n\n

System context: You are a senior PPC analyst reviewing Google Ads search term data.\n\nPrompt:\nBelow is a search term report for a [B2B SaaS \/ e-commerce \/ local service] account \nselling [product\/service]. Review all terms and:\n\n1. Identify clusters of irrelevant intent (e.g. \"free\", \"DIY\", \"student\", \"salary\")\n2. Flag informational queries unlikely to convert\n3. Highlight competitor brand names appearing in results\n4. Suggest negative keywords at campaign AND ad group level\n5. For each suggestion, note your confidence (high\/medium\/low) and reason\n\nDo NOT suggest any terms that could be valuable in a competitor conquest strategy.\n\n[PASTE SEARCH TERM DATA BELOW]<\/pre>\n\n\n\n
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As per The State of PPC Global Repor<\/a>t<\/a> 2026<\/a>, a survey conducted on about 1306 PPC professionals<\/a>:<\/p>\n\n\n\n
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Only 39% of practitioners are using AI for keyword research at all. The majority are still using it only for copy. The practitioners this post is written for \u2014 those using AI for search term analysis, exclusion strategy, and n-gram pattern recognition \u2014 are operating ahead of the majority. The risk at that level isn’t adoption. It’s shallow adoption: treating AI output as a finished list rather than a first draft to be verified against real account data.<\/p>\n<\/blockquote>\n\n\n\n
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Another interesting finding was that<\/p>\n\n\n\n
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“AI and automation” was the #1 priority for PPC professionals in 2024. By 2026 it has dropped to #3, behind improving campaign efficiency (68%) and generating conversions (59%). The novelty has worn off. What remains is practical daily adoption \u2014 which is precisely where the risk of complacency and shallow usage lives.<\/p>\n<\/blockquote>\n\n\n\n
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Tactic 2: Targeting Audience Demography Based<\/strong><\/h3>\n\n\n\n
For new campaigns with no data yet, ask the AI to think through who you don’t<\/em> want \u2014 not just what terms to exclude.<\/p>\n\n\n\n
Sample prompt \u2014 persona-based exclusions<\/p>\n\n\n\n
Prompt:\nI'm launching Google Search ads for [product]. My ideal customer is [description].\n\nDescribe 5 distinct persona types who might search for related terms but will NEVER \nconvert: their motivations, what they'd type into Google, and why they wouldn't buy.\n\nThen translate each persona into a list of negative keywords.\n\nFlag any terms that could also be high-intent for my actual audience so I can \ndecide whether to exclude them carefully.<\/pre>\n\n\n\n
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Tactic 3: The Close Variant Trap Audit<\/strong><\/h3>\n\n\n\n
Google’s close variant matching has become increasingly liberal, serving ads on semantic neighbours of your keywords. Ask AI to stress-test this.<\/p>\n\n\n\n
Sample prompt \u2014 close variant stress test<\/p>\n\n\n\n
Prompt:\nHere are my target keywords: [list].\n\nGoogle's close variant matching may serve my ads on semantically related terms \nI haven't considered. Generate 40 related terms that Google might consider \n\"close variants\" but which are irrelevant to my business. \n\nExplain the semantic reasoning for each so I can judge whether to add it as a negative.<\/pre>\n\n\n\n
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Tactic 4: The N-gram Anomaly Detector
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For mature accounts, use AI to identify single words appearing across many search terms that consistently waste budget \u2014 what the industry calls the “n-gram analysis.” This is a case where a dedicated tool often beats a general LLM, but if you’re doing it manually, AI can accelerate the interpretation step.<\/p>\n\n\n\n
Sample prompt \u2014 n-gram interpretation<\/p>\n\n\n\n
Prompt:
Here is a list of single words extracted from my search terms report,along with spend and conversions for each word:

Which words should I consider adding as negative keywords? Prioritise by wasted spend. Note any words where high spend but zero conversions might still deserve investigation before blocking (e.g. brand terms, navigational intent).<\/pre>\n\n\n\n
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The Honest Limitations (Updated for 2025)<\/strong><\/h2>\n\n\n\n
The original article flagged several limitations of ChatGPT. Two years on, the list has grown \u2014 and some limitations have shifted in significance.<\/p>\n\n\n\n
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No access to your conversion history<\/strong><\/h4>\n\n\n\n
This remains the single biggest limitation. An LLM can’t know that a search term generating 40% of your leads also contains the word “free.” Always pre-filter your SQR by performance before prompting any AI \u2014 or use a platform tool that has API access.<\/p>\n\n\n\n
Data privacy risk<\/strong><\/h4>\n\n\n\n
Pasting client search term data into a public AI interface is a contractual and ethical risk most agency contracts don’t account for. Anonymise data before sharing, or use tools with formal data processing agreements.<\/p>\n\n\n\n
Confident hallucinations<\/strong><\/h4>\n\n\n\n
AI will suggest negatives that sound sensible but could suppress high-intent traffic. The more niche your industry, the more likely it is to miss context. Always verify unusual suggestions against your actual SQR.<\/p>\n\n\n\n
Competitor terms flagged as irrelevant<\/strong><\/h4>\n\n\n\n
LLMs often suggest competitor brand names as negatives. This is a serious mistake if you’re running competitor conquest campaigns. Always build a protected list of competitor terms before running any AI-suggested negatives.<\/p>\n\n\n\n
Performance Max blindspot<\/strong><\/h4>\n\n\n\n
PMax campaigns have limited negative keyword support. AI-suggested negatives added at the campaign level may not behave as expected within PMax’s inventory. Understand the current negative keyword limitations for each campaign type before implementing.<\/p>\n\n\n\n
Close variant collateral damage<\/strong><\/h4>\n\n\n\n
Adding phrase or broad match negatives on the basis of AI suggestions can over-exclude. AI tends to suggest broad terms; in practice, exact match negatives are often safer as a starting point.<\/p>\n\n\n\n
It’s a moment-in-time snapshot<\/strong><\/h4>\n\n\n\n
AI brainstorming only reflects knowledge at generation time. New product launches, seasonal intent shifts, and emerging search behaviours won’t appear. Human review of live SQRs remains essential and non-negotiable.<\/p>\n\n\n\n
The complacency trap. <\/strong>The risk isn’t that AI is wrong \u2014 it’s that AI output looks authoritative and complete, so humans stop scrutinising it. Treat every AI-generated negative keyword list as a first draft requiring human review, not a finished deliverable.<\/p>\n\n\n\n
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What none of this means.<\/strong> It doesn’t mean don’t use it. The consequence isn’t “AI is bad for this work” \u2014 it’s that AI changes the nature<\/em> of the skill required. The new skill is knowing when to trust it, when to override it, how to prompt it well, and how to audit its output with real judgment. That’s harder to teach than reading a search term report, and most teams aren’t investing in building it deliberately.<\/p>\n<\/blockquote>\n\n\n\n
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So, When Should You Trust AI Output<\/strong><\/h2>\n\n\n\n
The answer to this lies in recognising the specific conditions under which AI output is reliable enough to act on \u2014 and the conditions where it isn’t. The core principle is simple: AI is trustworthy in proportion to how much of the relevant context it actually has access to.<\/strong> That narrows the use cases considerably.<\/p>\n\n\n\n
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Trust it more when…<\/strong><\/h3>\n\n\n\n
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Brainstorming with no stakes attached<\/strong><\/h4>\n\n\n\n
When generating a first-draft list for a new campaign with no live data, the cost of a wrong suggestion is zero \u2014 you review everything before anything goes live. This is AI’s strongest use case. Fast, broad, and the human review step is natural because there’s nothing else to compare against.<\/p>\n\n\n\n
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Pattern recognition on data you’ve already cleaned<\/strong><\/h4>\n\n\n\n
If you export a search term report, filter it to zero-conversion terms only, and then ask AI to cluster by intent \u2014 the AI is doing categorisation on data you’ve already screened. The risk of it suggesting you block something valuable is significantly lower because you removed the valuable queries before it ever sees them.<\/p>\n\n\n\n
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Industries and verticals you know well<\/strong><\/h4>\n\n\n\n
If you’ve worked in a vertical for years, you’ll immediately spot when an AI suggestion is wrong. Your expertise acts as the verification layer. The less you know the industry, the more dangerous it is to trust AI output \u2014 you lack the instinct to catch errors.<\/p>\n\n\n\n
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Structural and match-type questions<\/strong><\/h4>\n\n\n\n
Asking AI “should I add this as exact, phrase, or broad match negative given this context?” is a well-bounded question with defensible answers. It’s applying rules you could verify yourself \u2014 not drawing on proprietary data.<\/p>\n\n\n\n
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Formatting and organisation<\/strong><\/h4>\n\n\n\n
Using AI to take a messy list of candidate negatives and organise them by theme, remove duplicates, or structure them for upload is almost entirely safe. No judgment call is happening \u2014 just structure.<\/p>\n\n\n\n
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Don’t trust it when…<\/strong><\/h3>\n\n\n\n
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Conversion data is the deciding factor<\/strong><\/h4>\n\n\n\n
AI has no idea what converts in your account. A search term that looks irrelevant \u2014 “how does X work” \u2014 might be your highest-converting query because your audience researches before buying. AI will confidently suggest blocking it. Your data knows better.<\/p>\n\n\n\n
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Competitor and brand terms are involved<\/strong><\/h4>\n\n\n\n
AI will frequently flag competitor brand names as irrelevant because they don’t match your product description. Whether to exclude or target them is a strategic question requiring your business context, not pattern matching.<\/p>\n\n\n\n
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The industry is niche or technical<\/strong><\/h4>\n\n\n\n
The more specialised the domain, the more likely AI is to misread intent. A query that looks like a DIY search might be entirely professional in industries where professionals use informal language. AI trained on general internet text has no way to know this.<\/p>\n\n\n\n
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You haven’t read every item on the list<\/strong><\/h4>\n\n\n\n
This sounds trivial but it’s the most common failure mode. If you’re approving AI-generated negative keyword lists by skimming rather than reading each item, you’re not using AI as a tool \u2014 you’re delegating decisions to it. The trust question becomes moot because no real verification is happening.<\/p>\n\n\n\n
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The four-question test. <\/strong> Before acting on any AI suggestion, ask: (1) Does the AI have my actual performance data, or is it working from pattern matching alone? (2) Can I explain the reasoning well enough to defend this decision to a client? (3) Have I cross-referenced against my protected lists \u2014 converting terms, competitor terms, seasonal terms? (4) Am I reviewing this because I expect to catch errors, or because I expect to approve most of it? If the answer to four is the latter, the review isn’t real.<\/p>\n<\/blockquote>\n\n\n\n
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A Suggested Workflow: AI + Human Judgment
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We recommend trying a lean five-step loop that keeps AI in its lane and human judgment at every decision gate. It is deliberately simple. The right workflow for your account will depend on your vertical, team size, and how much live data you have. Two alternative frameworks worth comparing against are linked at the bottom of this section.<\/p>\n\n\n\n