Automating Google Ads Negative Keywords With LLMs

Reviewing search term reports manually is a reactive chore that costs you thousands before you even open the spreadsheet. Thanks to semantic matching, broad match keywords frequently trigger on irrelevant queries, quietly draining budget while your team wastes hours on line-by-line analysis.

To automate search term auditing, you connect a large language model to your Google Ads data, prompt it to classify intent, and output a negative keyword list. Proper implementation stops the budget drain caused by irrelevant queries, which a WordStream study notes can consume 20% to 30% of standard advertising budgets.

Connecting the LLM to search data

When building the operational foundations outlined in The Agency Guide to AI-Driven Google Ads Performance, the first step is plumbing. To audit search terms automatically, the language model needs a reliable pipeline to access your campaign data.

The most robust method utilizes the Model Context Protocol (MCP). According to ppc.io, Google open-sourced an official Google Ads MCP server in October 2025. This protocol allows language models like Claude or Gemini to query the Google Ads API directly using plain English. A marketer can simply ask the model to pull search terms with more than $100 in spend and zero conversions. The model translates that request into Google Ads Query Language, executes the data pull, and retrieves the terms for analysis.

For marketers looking to build autonomous scheduled workflows, workflow automation tools offer a practical alternative. A system blueprint shared by Cody Schneider on LinkedIn demonstrates how to use n8n to connect the Google Ads API to OpenAI. Triggered daily at midnight, the pipeline automatically fetches the top 500 search terms by spend over the previous seven days, calculates metrics like click-through rate and cost per conversion, and passes the formatted payload to GPT-4o for auditing.

If you lack developer resources, you can still execute this process manually. Ryze AI notes that practitioners can export a search term CSV from Google Ads and upload it directly into Claude Projects. While this manual method takes about five minutes per session, it requires zero technical configuration and still allows the model to process thousands of rows of search data against your custom instructions.

Pre-filtering to control API costs

Passing your entire search term database directly to a frontier language model is highly inefficient. Mature accounts generate hundreds of thousands of unique queries, and analyzing all of them via API calls creates unnecessary latency and cost. The most effective systems filter the data heavily before the LLM ever sees it.

The architecture developed by 8020brain utilizes a hybrid 10-step processing pipeline. Rather than analyzing everything, the system runs free linguistic and volume checks first. It strips out non-Latin characters, removes navigational competitor searches, and isolates the bottom 5% of terms by volume. The system only sends the top 500 to 2,000 high-impression terms to the language model. By leveraging machine learning propagation, the system learns patterns from the LLM outputs and applies them to the rest of the database, achieving 97% classification coverage while keeping API costs near zero.

For enterprise scale, custom models offer another route to efficiency. Researchers at Walmart AdTech published a study on the Enhancement of E-commerce Sponsored Search Relevancy with LLM, where they fine-tuned a LLaMA2 7B model using Low-Rank Adaptation. This smaller, specialized model evaluated user search queries against advertised product titles, categorizing them as relevant, partially relevant, or irrelevant. The fine-tuned architecture achieved 89.43% accuracy, outperforming larger, more expensive models like GPT-4 on narrow search classification tasks.

Structuring the classification prompt

Language models are creative by default, which is a liability when you need deterministic data classification. A poorly structured prompt will return conversational paragraphs instead of a clean, uploadable list of negative keywords.

To enforce strict outputs, you must configure the model parameters and provide rigid contextual framing. A LinkedIn Learning tutorial on Google Ads automation recommends setting the model's temperature parameter to zero to eliminate creative variance, and capping the maximum tokens to ten to force brief responses. The prompt should assign a persona, ground the model in your specific business geography, and feed it your existing negative keyword list to prevent duplicate recommendations.

Next, you need to define explicit performance thresholds. Toffu AI suggests instructing the model to flag intent mismatches, along with hard metric rules like isolating search terms that have logged 50 clicks with zero conversions, or queries with a click-through rate below 1%. The model should classify flagged terms into distinct categories, such as "competitor research," "informational," or "off-topic."

You can also prompt the AI to perform geographic and demographic filtering. Instruct the model to scan for location modifiers outside your service area, or demographic qualifiers that do not match your ideal customer profile. Filtering out irrelevant demographic qualifiers ensures your search traffic aligns with the structural frameworks discussed in How to Map Buyer Personas to AI Campaign Structures. Instruct the model to return exactly one word per search term—either "Negative" or "N/A"—so the output requires no further parsing.

Applying negatives safely at scale

Identifying wasted spend is only half the workflow; you must also push those exclusions back to the ad platform. However, allowing an AI agent to alter campaign targeting autonomously carries significant risk, which is why system boundaries are critical.

According to ppc.io, the official Google Ads MCP server is strictly read-only by design. It can pull data and format lists, but it cannot write negative keywords to your account. To automate the final step, you must use a direct API integration to hit the Google Ads mutate endpoint. The n8n workflow outlined by Cody Schneider handles this by using a code node to filter out low-confidence AI suggestions, retaining only high-confidence matches. An HTTP request then pushes the approved negative keywords to the campaign.

Even with automation in place, human oversight remains necessary. Fisher Digital notes that while shifting just 2% of impressions away from zero-value terms yields exponential compounding benefits over time, advertisers should always manually review AI suggestions before applying them. A common safeguard is injecting a form node or a Slack notification into the pipeline, pausing the automated push until a practitioner clicks approve.

This is precisely how SproutMe agents operate. Our workspace uses explicit guardrails that cap what an agent can execute independently. The system monitors your search term reports continuously and drafts negative keyword exclusions based on your unified business context and past memory. However, the agent requires human approval before it pushes the final exclusions to your live campaigns, ensuring you maintain ultimate control over targeting decisions.

Conclusion

Automating search term auditing with a large language model transforms a tedious, error-prone manual task into a continuous optimization engine. By connecting your Google Ads data to an LLM, pre-filtering low-volume queries, and enforcing strict prompt parameters, you can systematically identify and categorize irrelevant traffic. While the technology can seamlessly draft the exclusions, retaining human approval at the final stage ensures your budget is protected without compromising strategic control.