{"id":41652,"date":"2026-05-20T12:44:17","date_gmt":"2026-05-20T12:44:17","guid":{"rendered":"https:\/\/adspyder.io\/blog\/?p=41652"},"modified":"2026-05-20T13:10:29","modified_gmt":"2026-05-20T13:10:29","slug":"competitor-ads-ai-ad-prompts","status":"publish","type":"post","link":"https:\/\/adspyder.io\/blog\/competitor-ads-ai-ad-prompts\/","title":{"rendered":"How to Use Competitor Ads to Train Better AI Ad Prompts \u2014 May 2026"},"content":{"rendered":"
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AI Ads & Automation<\/span><\/div>\n

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Quick Answer<\/p>\n

Pull 15\u201320 competitor ads from AdSpyder’s Ad Library<\/a>, extract the hook pattern, offer mechanic, emotional trigger, and CTA verb from each, then feed those as a structured context block into AdSpyder’s Text Ad Generation<\/a>. You’re not copying ads \u2014 you’re encoding what the market has already validated as prompt context, so your AI starts with competitive intelligence, not a blank slate.<\/p>\n<\/div>\n

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Most marketers who use AI to write ads treat the prompt like a blank order form. They type a product name, a vague audience description, and a tone request \u2014 then wonder why the output sounds like it has never seen an actual ad before.<\/p>\n

The problem isn’t the AI. Generic input produces generic output. Your competitors have spent real budget testing what makes your audience stop, click, and convert. That signal is sitting in their live ads right now \u2014 visible, searchable, and structured enough to extract in 15 minutes.<\/p>\n

This is a technical-creative workflow. By the end you’ll have a repeatable five-step process, a prompt template, and a data-backed answer to a question most AI-first teams get wrong: should you research before you generate, or after?<\/strong> AdSpyder’s own platform data has a clear, contrarian answer \u2014 and it should change how you sequence your entire ad creation workflow.<\/p>\n

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85.6%<\/p>\n

skip research entirely<\/p>\n

of text-ad generators ran zero competitor searches before first AI draft<\/p>\n<\/div>\n

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675<\/p>\n

avg. competitor ads reviewed<\/p>\n

by the 14% who did research before generating<\/p>\n<\/div>\n

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64%<\/p>\n

same-day search + generate<\/p>\n

of generation sessions included a competitor search on the same calendar day<\/p>\n<\/div>\n

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400M+<\/p>\n

ads indexed<\/p>\n

across 10 platforms \u2014 the competitive context behind every prompt<\/p>\n<\/div>\n<\/div>\n

AdSpyder platform data, May 2026. Telemetry window: Aug 2023 \u2013 Jul 2025.<\/p>\n

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88,000+ competitor-ad searches. 2,000+ AI text ads generated. One platform.<\/p>\n

Research what’s already working in your market \u2014 then generate smarter first drafts.<\/p>\n

Try Text Ad Generation Free \u2192<\/a><\/p>\n<\/div>\n

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In This Guide<\/p>\n

\ud83d\udcca Research order: the data<\/a>
\n\ud83d\uddbc\ufe0f Image vs text ad difference<\/a>
\n\ud83c\udfaf The 4 signals to extract<\/a>
\n\u2699\ufe0f 5-step workflow<\/a>
\n\ud83d\udcdd Prompt template<\/a>
\n\ud83c\udf10 Which platform to spy first<\/a>
\n\u2696\ufe0f Generic vs competitor-trained<\/a>
\n\u26a0\ufe0f Mistakes to avoid<\/a>
\n\u2705 Pre-prompt checklist<\/a>
\n\u2753 FAQ<\/a><\/div>\n<\/div>\n
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The Research-First Problem: AdSpyder’s Own Users Get This Wrong<\/h2>\n

There is a specific number from AdSpyder’s platform data that should change how you sequence your AI ad workflow.<\/p>\n

Across two years of usage (Aug 2023 \u2013 Jul 2025), 1,286 AdSpyder users generated a text ad using the platform’s AI generation feature. Of those, 85.6% had run zero Ad Library searches before their first generation.<\/strong> They opened the generator, typed a prompt, and expected market-ready output \u2014 with no market data attached.<\/p>\n

Here’s the more important breakdown. Only 346 users (26.9%) never searched competitor ads at all. A much larger group \u2014 755 users, or 58.7% \u2014 generated first and then searched. They used competitor research as a sanity check after the draft, not as input before it.<\/p>\n

That ordering difference is the entire point of this blog. Research done after a draft catches what’s wrong. Research done before shapes what gets written. Same information, completely different function.<\/p>\n

\n\n\n\n\n\n\n\n\n\n
User behaviour<\/th>\nUsers (n)<\/th>\nShare<\/th>\nWhat this means<\/th>\n<\/tr>\n<\/thead>\n
Generated first, searched after<\/td>\n755<\/td>\n58.7%<\/td>\nUsing research to critique, not to create<\/td>\n<\/tr>\n
Never searched at all<\/td>\n346<\/td>\n26.9%<\/td>\nGenerating with zero market context<\/td>\n<\/tr>\n
Searched 1\u20135 times before generating<\/td>\n102<\/td>\n7.9%<\/td>\nLight research before generating<\/td>\n<\/tr>\n
Searched 6\u201320 times before generating<\/td>\n54<\/td>\n4.2%<\/td>\nModerate research before generating<\/td>\n<\/tr>\n
Searched 21+ times before generating<\/td>\n29<\/td>\n2.3%<\/td>\nDeep research before generating<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

Source: AdSpyder platform data, May 2026. Base: 1,286 users who generated a text ad. Telemetry window Aug 2023 \u2013 Jul 2025.<\/p>\n

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The 64% same-session signal<\/p>\n

On days when a user generated a text ad, 64% of those calendar days also included at least one Ad Library search by the same user. So competitor research is happening \u2014 it’s just happening after<\/em> the first draft in the vast majority of cases. Front-loading that research is the single highest-leverage change to this workflow.<\/p>\n<\/div>\n

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One Finding That Changes the Advice: Image Ad Creators Do This Differently<\/h2>\n

The 85.6% stat above applies to text ad generators. Image ad generators behave in almost the opposite pattern \u2014 and that contrast is the most instructive data point in this blog.<\/p>\n

Of the 171 users who generated an image ad on AdSpyder, 62% had already searched the Ad Library before their first image generation. Only 19.3% skipped research entirely. Compare that to text ads, where 85.6% skip research. The direction reverses completely.<\/p>\n

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Text Ad Generators<\/p>\n

85.6%<\/p>\n

skip research before first generation<\/p>\n<\/div>\n

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Image Ad Generators<\/p>\n

62%<\/p>\n

had already searched before generating<\/p>\n<\/div>\n<\/div>\n

The likely reason: image production has a higher perceived cost. When you’re about to invest time in a visual asset, you want to know what’s already in market before you start. Text feels cheaper to iterate \u2014 so people generate first and refine later.<\/p>\n

The argument for text-ad creators is exactly the same logic applied to copy: front-loading research costs 15 minutes and improves the quality of your first draft. The cost of skipping it shows up in revision rounds, approval friction, and test cycles that could have been avoided.<\/p>\n

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The 4 Signals Worth Extracting from Competitor Ads<\/h2>\n

Not everything in a competitor’s ad is worth encoding into your prompt. Brand names, specific pricing, and proprietary claims are noise. What you’re extracting are the structural patterns that work independently of the brand \u2014 the skeleton of the ad, not its skin.<\/p>\n

These four signal types consistently improve AI output quality when included as prompt context:<\/p>\n

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\u2460 Hook Pattern<\/p>\n

The structural formula the opening line uses: question, stat shock, negative contrast, bold claim, direct command, or problem-first. This is the most transferable signal \u2014 it tells you what your audience is already conditioned to respond to on that platform.<\/p>\n<\/div>\n

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\u2461 Offer Mechanic<\/p>\n

How the value is packaged: free trial, money-back guarantee, limited seats, feature-first, outcome-first, comparison anchor, or bonus. If three competitors lead with a free trial and your prompt doesn’t mention yours, you’re already behind on offer parity.<\/p>\n<\/div>\n

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\u2462 Emotional Trigger<\/p>\n

The primary emotion being activated: fear of missing out, aspiration, frustration relief, social proof, or urgency. If the category runs on anxiety copy and you prompt for “confident and friendly” tone, your ad will feel off-market \u2014 even if the copy is technically good.<\/p>\n<\/div>\n

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\u2463 CTA Verb<\/p>\n

The action word your category uses: “Get”, “Start”, “Try”, “Book”, “See”, “Claim”, “Join”. Each carries a different friction signal. “Book a call” = high intent. “See how it works” = low friction. Mismatching this to your funnel stage costs clicks.<\/p>\n<\/div>\n<\/div>\n

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How many is enough?<\/p>\n

You don’t need to extract all four from every ad. Scan 15\u201320 ads, and when the same hook pattern appears in 12 of them, that pattern is your market’s baseline. Your AI output needs to at least match that baseline before it can beat it. The 14% of AdSpyder users who research before generating review roughly 675 ads in aggregate \u2014 enough to spot patterns, not enough to drown in them.<\/p>\n<\/div>\n

\"The<\/p>\n

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The 5-Step Workflow: Competitor Ad to AI-Generated Draft<\/h2>\n

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1<\/div>\n
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Pull 15\u201320 competitor ads from AdSpyder’s Ad Library<\/h3>\n

Open AdSpyder Ad Library<\/a> and search by your primary keyword \u2014 not your brand name, your target keyword. Apply your target platform filter first. Sort by “Most Recent” so you’re working from live market data, not 2021 archive copy.<\/p>\n

Aim for 5\u20138 different advertisers, not 20 ads from one competitor. One advertiser running 20 variants tells you about their testing cadence, not about market patterns. You need breadth to spot frequency.<\/p>\n<\/div>\n<\/div>\n

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2<\/div>\n
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Classify each ad across the 4 signal types<\/h3>\n

For each ad, note: (1) hook pattern, (2) offer mechanic, (3) emotional trigger, (4) CTA verb. A plain text list works. You’re building a frequency map \u2014 which hook type appears most across the set? Which CTA verb dominates?<\/p>\n

When you see the same combination \u2014 say, “question hook + free trial + urgency + Get CTA” \u2014 appearing in 8 of 20 ads, that’s the category’s default script. Your AI prompt needs to know about it whether you’re matching it or deliberately breaking from it.<\/p>\n<\/div>\n<\/div>\n

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3<\/div>\n
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Write a competitor context block \u2014 not a creative brief<\/h3>\n

A creative brief tells the AI what you want your ad to say. A competitor context block tells the AI what the market already looks like. These are different inputs and they produce different outputs.<\/p>\n

The context block is 4\u20136 lines. It names the dominant patterns, flags what’s over-indexed in the category (so you can differentiate), and identifies the one gap no competitor’s ad currently fills. See the exact template in the next section.<\/p>\n<\/div>\n<\/div>\n

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4<\/div>\n
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Feed the context block into AdSpyder’s Text Ad Generation<\/h3>\n

In AdSpyder’s Text Ad Generation<\/a>, paste your context block before your core product description. The generator uses it as conditioning \u2014 producing copy that’s aware of the competitive landscape, not just your product attributes.<\/p>\n

Request at least 3 variants: one that matches the category’s dominant hook pattern, one that breaks from it, and one that leads with your differentiation gap. You need contrast to judge quality at all.<\/p>\n<\/div>\n<\/div>\n

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5<\/div>\n
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Validate each variant against the competitor benchmark<\/h3>\n

Before any variant goes to test, hold it against your frequency map. Does the hook pattern match or deliberately exceed the category baseline? Is the offer mechanic competitive? Does the CTA verb match the funnel stage you’re targeting?<\/p>\n

This isn’t about copying what competitors do \u2014 it’s about not accidentally writing below the market’s quality floor. An ad that fails the benchmark on all four signals goes back to the generator with tighter context.<\/p>\n<\/div>\n<\/div>\n

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The scale behind this workflow<\/p>\n

AdSpyder users have run 88,000+ competitor-ad searches, generated 139,000+ keyword suggestions, and set up 8,600+ active tracking projects since launch. The research infrastructure is already there. The gap is connecting it to the generation step before the first draft, not after. (AdSpyder platform data, May 2026)<\/p>\n<\/div>\n

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The Competitor Context Block: Exact Template and How to Fill It<\/h2>\n

Here is the structure. The yellow-highlighted fields are what you fill in after your competitor research. The instruction to the AI goes at the end \u2014 after the context, not before it.<\/p>\n

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Competitor Context Block \u2014 paste before your product description<\/p>\n

MARKET CONTEXT (do not copy \u2014 use as AI conditioning):<\/p>\n

Dominant hook pattern in this category:
\n[e.g. “Question hook \u2014 ‘Are you still paying for X without Y?'” or “Stat-shock \u2014 ‘Most teams waste 6 hrs\/week on this'”]<\/span><\/p>\n

Most common offer mechanic:
\n[e.g. “Free trial \u2014 12 of 20 competitor ads lead with this; nobody uses a money-back guarantee angle”]<\/span><\/p>\n

Primary emotional trigger used:
\n[e.g. “Frustration relief \u2014 competitors focus on time wasted on manual work, not on aspiration”]<\/span><\/p>\n

Dominant CTA verb:
\n[e.g. “‘Start’ or ‘Get’ dominate \u2014 low-friction entry language is the norm in this category”]<\/span><\/p>\n

Gap no competitor addresses:
\n[e.g. “None mention platform-specific coverage \u2014 every ad is generic ‘all ads in one place’, no one claims YouTube or LinkedIn specifically”]<\/span><\/p>\n

Instruction to AI (goes after context, not before):<\/p>\n

Write 3 Google Search ad variants for [PRODUCT]<\/span> targeting [AUDIENCE]<\/span>.
\nVariant 1: match the dominant pattern above (benchmark).
\nVariant 2: keep the same offer mechanic but break the hook pattern.
\nVariant 3: lead with the gap no competitor addresses (differentiation).
\nFormat: 3 headlines (max 30 chars each), 2 descriptions (max 90 chars each) per variant.<\/p>\n<\/div>\n

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Critical: never paste raw competitor copy<\/p>\n

Never put actual competitor ad text into the context block. You’re passing structural patterns, not creative text. Structural patterns are market intelligence. Raw copy is a reproduction risk. “Question hook” is a pattern. “Are you tired of paying for ads that don’t work?” is someone else’s copy.<\/p>\n<\/div>\n

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<\/p>\n

Which Platform Should You Spy on First?<\/h2>\n

Match your research platform to your target platform. Cross-platform research adds noise more than signal \u2014 Facebook copy patterns are structurally different from Google search patterns, and conflating them weakens your context block.<\/p>\n

Here’s how AdSpyder users distribute their competitor research \u2014 which tells you where the pattern signal is most concentrated:<\/p>\n

\n\n\n\n\n\n\n\n\n\n
Platform<\/th>\nShare of searches<\/th>\nBest signal to extract<\/th>\nBest for<\/th>\n<\/tr>\n<\/thead>\n
Google Search<\/td>\n50%<\/td>\nCTA verb + offer mechanic<\/td>\nIntent-led copy, keyword-aligned hooks, best starting point for any campaign<\/td>\n<\/tr>\n
Facebook \/ Meta<\/td>\n22%<\/td>\nHook pattern + emotional trigger<\/td>\nScroll-stop copy, emotional angle, visual-copy relationship<\/td>\n<\/tr>\n
YouTube<\/td>\n15%<\/td>\nOffer mechanic + social proof signals<\/td>\nLong-form narrative structure, credibility framing<\/td>\n<\/tr>\n
LinkedIn<\/td>\n2.7%<\/td>\nAudience-specific hook + CTA verb<\/td>\nB2B positioning, job-function targeting language, long-copy norms<\/td>\n<\/tr>\n
Bing + others<\/td>\n~9%<\/td>\nOffer mechanic confirmation<\/td>\nCross-validation of Google search patterns<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

Source: AdSpyder Ad Library search distribution, AdSpyder platform data May 2026.<\/p>\n

Google’s 50% share is not just because it’s the biggest platform. Search copy is the most structurally readable \u2014 every element has a defined slot (headline, description, CTA). Patterns are easier to isolate and extract. If you’re running mixed-platform campaigns and need to start somewhere, start with Google.<\/p>\n

For B2B campaigns, the LinkedIn Ad Library on AdSpyder<\/a> gives you audience-segment language and long-copy norms that no other platform’s data can provide. The context block you build from LinkedIn research will look structurally different \u2014 and that difference matters for B2B prompts.<\/p>\n

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<\/p>\n

Generic Prompting vs Competitor-Trained Prompting: What Actually Changes<\/h2>\n

This isn’t a marginal quality improvement. The output type changes, not just the output quality. Here’s how the three main prompting approaches compare in practice:<\/p>\n

\n\n\n\n\n\n\n\n
Workflow<\/th>\nWhat the AI knows<\/th>\nOutput type<\/th>\nMain risk<\/th>\n<\/tr>\n<\/thead>\n
Generic prompt
\n“Write a Google ad for [product]”<\/span><\/td>\n		Your product attributes only<\/td>\nPolished but generic \u2014 could be for any brand in any category<\/td>\nBelow-market quality floor; multiple revision rounds<\/td>\n<\/tr>\n
Manual competitor review + generic prompt
\nInformal research, then standard prompt<\/span><\/td>\n		Your product + scattered observations in your head, not in the prompt<\/td>\nSlightly better instinct \u2014 but research never entered the prompt<\/td>\nResearch investment is wasted; output doesn’t reflect what you learned<\/td>\n<\/tr>\n
AdSpyder research + competitor context block
\nStructured patterns extracted, then fed as context<\/span><\/td>\n		Your product + the market’s current baseline + the gap you can own<\/td>\nMarket-aware copy \u2014 positioned against real competition from the first draft<\/td>\nTakes 15 extra minutes upfront; pays back across every revision cycle<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

The middle row is the most common failure mode: marketers who do the research but don’t encode it into the prompt. The research investment disappears at the prompt boundary.<\/p>\n

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<\/p>\n

4 Mistakes That Waste Your Competitor Research<\/h2>\n
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Researching only one competitor<\/p>\n

One advertiser’s pattern tells you about their strategy, not the market. You need 5\u20138 advertisers to identify a frequency pattern. Researching only the category leader means you’re building context around a single brand voice, not a market signal.<\/p>\n<\/div>\n

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Feeding copy instead of structure<\/p>\n

Pasting three competitor headlines into a prompt asks for a remix. Describing the structural pattern (“stat-shock hook, free-trial offer, ‘Start’ CTA”) gives the AI market intelligence. One produces derivative copy; the other produces market-aware copy.<\/p>\n<\/div>\n

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Using outdated ads as research material<\/p>\n

An ad pulled 18 months ago was pulled for a reason. Sort by recency first. AdSpyder’s URL and Domain Analysis<\/a> shows currently active ads per advertiser domain \u2014 the filter that keeps your context block current, not historical.<\/p>\n<\/div>\n

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Cross-platform research for single-platform campaigns<\/p>\n

Facebook copy patterns don’t transfer cleanly to Google search. If you’re generating Google ads, research Google. The patterns differ enough that cross-platform context adds noise to your prompt rather than signal. Match the research surface to the target surface.<\/p>\n<\/div>\n<\/div>\n

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<\/p>\n

Pre-Prompt Checklist \u2014 Before You Open the Generator<\/h2>\n

Run through this before every AI ad generation session. It takes 2 minutes and replaces 2 hours of revision:<\/p>\n

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\u2713<\/span><\/p>\n

Searched 15\u201320 competitor ads across 5+ advertisers on the target platform (not your own brand)<\/p>\n<\/div>\n

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\u2713<\/span><\/p>\n

Identified the dominant hook pattern across at least 8 of those ads<\/p>\n<\/div>\n

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\u2713<\/span><\/p>\n

Mapped the most common offer mechanic and CTA verb across the competitive set<\/p>\n<\/div>\n

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\u2713<\/span><\/p>\n

Identified the primary emotional trigger dominating the category<\/p>\n<\/div>\n

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\u2713<\/span><\/p>\n

Found at least one positioning gap no competitor’s ad currently fills<\/p>\n<\/div>\n

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\u2713<\/span><\/p>\n

Written a competitor context block using structural patterns \u2014 no raw competitor copy in the prompt<\/p>\n<\/div>\n

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\u2713<\/span><\/p>\n

Requested 3 variants: one matching the baseline, one breaking it, one leading with the differentiation gap<\/p>\n<\/div>\n

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\u2713<\/span><\/p>\n

Validated each output variant against the frequency map before any variant goes to test<\/p>\n<\/div>\n<\/div>\n

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<\/p>\n

Frequently Asked Questions<\/h2>\n
\nCan I paste competitor ads directly into an AI prompt? +<\/span><\/summary>\n

Not the actual copy \u2014 that’s a reproduction risk. Extract structural elements instead: the hook type, the offer mechanic, the CTA verb, the emotional trigger. Feed those as context. The AI uses them as market conditioning, not as source material to rework. “Question hook” is a pattern and safe to pass in; a competitor’s actual headline text is not.<\/p>\n<\/details>\n

\nHow many competitor ads should I review before generating? +<\/span><\/summary>\n

The 14% of AdSpyder users who do research before generating review roughly 675 ads in aggregate before their first generation. You don’t need to read all of them in detail \u2014 scan enough to spot 3\u20135 repeating patterns across 5\u20138 different advertisers. In practice, 15\u201320 ads usually surfaces the dominant patterns clearly enough to write a solid context block.<\/p>\n<\/details>\n

\nWhich platform’s competitor ads are most useful for prompt training? +<\/span><\/summary>\n

Match your research to your target platform. AdSpyder users search Google 50% of the time, Facebook 22%, YouTube 15%. Google search copy is the most structurally readable and the best starting point, even for mixed-platform campaigns. For LinkedIn campaigns specifically, do your research in the LinkedIn Ad Library \u2014 B2B copy patterns are distinct enough that Google research won’t transfer cleanly.<\/p>\n<\/details>\n

\nDoes AdSpyder’s AI generation support competitor-context prompts? +<\/span><\/summary>\n

Yes. AdSpyder’s Text Ad Generation accepts structured prompt context including hook patterns, offer mechanics, tone descriptors, and CTA preferences extracted from Ad Library research. The workflow in this blog \u2014 research, context block, then generate \u2014 is exactly what the feature is designed to support.<\/p>\n<\/details>\n

\nWhy do image ad creators research before generating but text ad creators don’t? +<\/span><\/summary>\n

From AdSpyder’s platform data: 62% of image ad generators had searched the Ad Library before their first generation, vs just 14.4% of text ad generators. The most likely reason: image work carries a higher perceived production cost, so creators want to see what’s already in market before committing to a visual direction. Text feels cheap to iterate \u2014 so people generate first and refine later. The argument for applying the same pre-research discipline to text is identical: it costs 15 minutes and improves first-draft quality significantly. (AdSpyder platform data, May 2026)<\/p>\n<\/details>\n

<\/p>\n

\n

AdSpyder Ad Library + Text Ad Generation<\/p>\n

400M+ competitor ads. One AI generation workflow.<\/p>\n

Search what competitors are running across Google, Facebook, YouTube, LinkedIn, and 6 more platforms \u2014 then feed those patterns as structured context into your next AI ad draft. The research and generation live in the same platform.<\/p>\n

Try Text Ad Generation Free \u2192<\/a>
\nExplore the Ad Library<\/a><\/div>\n

23,000+ users \u00b7 10 platforms \u00b7 400M+ ads indexed \u00b7 No credit card required<\/p>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

AI Ads & Automation Quick Answer Pull 15\u201320 competitor ads […]<\/p>\n","protected":false},"author":27,"featured_media":41655,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[181,371],"tags":[],"class_list":["post-41652","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ads-set-up","category-competitors-research"],"acf":[],"yoast_head":"\nHow to Use Competitor Ads as AI Ad Prompt Context - May 2026<\/title>\n<meta name=\"description\" content=\"Pull hook patterns, offer mechanics, and CTA language from competitor ads in AdSpyder \u2014 and generate market-aware copy from your first draft.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/adspyder.io\/blog\/wp-json\/wp\/v2\/posts\/41652\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to Use Competitor Ads as AI Ad Prompt Context - 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