As LLM products mature, monetization through advertising is becoming inevitable. This post surveys the landscape of ad formats—separating what’s already deployed in production from what remains in the research stage.
In Production
1. Sponsored Follow-Up Questions (Perplexity)
Status: ✅ Live since November 2024
Perplexity introduced “sponsored related questions” that appear after the AI’s answer:
User: "What's the best laptop for students?"
AI: [Detailed answer about laptop features...]
📌 Related questions:
• What are the best laptop deals under $500? [Sponsored]
• How does the MacBook Air M3 compare? [Sponsored by Apple]
• Which laptops have the best battery life?How it works:
- Ads appear as suggested follow-up questions
- Clearly labeled as “Sponsored”
- Brands pay for placement in relevant query categories
- Users choose whether to click
Why it works: Low intrusiveness—the main answer is untouched, ads are optional next steps.
2. Sponsored Sources/Citations (Google AI Overviews)
Status: ✅ Live in Search
Google’s AI Overviews include shopping ads and sponsored links in the source citations:
AI Overview: "The best running shoes for marathons include..."
Sources:
🛒 [Sponsored] Nike.com - Nike Vaporfly 3
🛒 [Sponsored] ASICS.com - Metaspeed Sky+
📄 Runner's World - 2024 Marathon Shoe Guide
📄 Reddit r/running - Community recommendationsHow it works:
- Existing Google Shopping ads surface in AI Overview citations
- Sponsored results mixed with organic sources
- Leverages existing ad auction infrastructure
3. Side Panel Ads (Microsoft Copilot/Bing)
Status: ✅ Live
Microsoft Copilot displays traditional Bing ads alongside AI responses:
┌─────────────────────────────────────────┬──────────────────┐
│ │ │
│ Copilot response about coffee makers │ [Ad] │
│ comparing features and prices... │ Nespresso │
│ │ VertuoPlus │
│ │ $199 → $149 │
│ │ Shop Now → │
│ │ │
└─────────────────────────────────────────┴──────────────────┘How it works:
- Traditional display ads shown in sidebar
- Clear visual separation from AI content
- Leverages existing Bing Ads infrastructure
Trade-off: Lower engagement but maximum transparency.
4. Affiliate Links in Responses (Various)
Status: ✅ Live in multiple products
Many AI assistants include affiliate links when recommending products:
User: "What's a good beginner guitar?"
AI: "For beginners, I recommend the Yamaha FG800.
You can find it on [Amazon](https://amazon.com/dp/...) for around $200."How it works:
- AI includes product links with affiliate tracking
- Revenue share when users purchase
- Often not explicitly disclosed as advertising
Examples: Various AI shopping assistants, review sites with AI chat.
5. Brand Partnerships/Sponsored Content (ChatGPT Plugins Era)
Status: ⚠️ Limited/Deprecated
During the ChatGPT plugins era, brands like Expedia, Instacart, and Kayak had direct integrations:
User: "Find me a flight to Tokyo"
AI: [Powered by Kayak]
"I found several options for you:
• ANA - $850 round trip, 1 stop
• JAL - $920 round trip, direct
[Book on Kayak →]"Current status: Plugins deprecated in favor of GPTs and Actions, but brand partnerships continue in various forms.
In Research / Experimental
1. Contextual In-Response Ad Injection
Status: 🔬 Research (GEM-Bench)
Embedding ads directly within the AI’s response text:
User: "Plan a 3-day trip to Paris"
AI: "Day 1: Start at the Eiffel Tower. For a unique experience,
[GetYourGuide](https://getyourguide.com) offers skip-the-line
tickets with local guides... Day 2: Visit the Louvre..."Research findings (GEM-Bench, arXiv:2509.14221):
- Simple prompt injection: Good CTR, but reduced user satisfaction
- Post-generation refinement: Better UX, but adds computational overhead
- Key challenge: Making ads feel natural, not intrusive
Why not in production yet: User trust concerns—studies show users feel manipulated when ads are embedded without clear disclosure.
2. Commercial Intent Retrieval (RARE Framework)
Status: 🔬 Research → Production at scale (Alibaba)
The RARE paper introduced “Commercial Intentions” for real-time ad retrieval:
User Query: "comfortable shoes for standing all day"
↓
LLM generates Commercial Intent: "ergonomic work footwear,
cushioned insoles, nurse shoes, retail worker shoes"
↓
Retrieved Ads: [Nike Air Max, Crocs Work, Skechers Go Walk]Production results (deployed in search advertising):
- +5.04% consumption
- +6.37% GMV
- +1.28% CTR
Status note: While the paper is research, the system is deployed in Alibaba’s ad infrastructure.
3. Generative Engine Optimization (Rewrite-to-Rank)
Status: 🔬 Research (arXiv:2507.21099)
The inverse of ad injection—optimizing brand content to be cited by LLMs:
Traditional SEO: GEO (Generative Engine Optimization):
──────────────── ────────────────────────────────────
Optimize for Google Optimize for LLM retrieval
Keyword density Semantic richness
Meta tags Answer implicit questions
Backlinks Context that triggers citationsExample transformation:
| Before | After (GEO-optimized) |
|---|---|
| “Nike Air Max 90 - Air cushioning" | "For users seeking comfortable everyday running shoes with responsive cushioning, the Nike Air Max 90 offers visible Air technology ideal for extended wear” |
Key insight: The second version is more likely to be retrieved AND cited by RAG-based LLMs because it directly answers potential user queries.
Why it matters: Brands are already investing in GEO to ensure their products appear in AI-generated responses—this is the supply side of the GEM ecosystem.
4. LLM-Generated Persuasive Ads
Status: 🔬 Research (arXiv:2512.03373)
Using LLMs not just to inject ads, but to generate more persuasive ad content:
Key findings:
- LLM-generated ads match human ads in personalization (Big Five personality targeting)
- LLM-generated ads outperform humans in persuasive storytelling
- Particularly effective: authority appeals, social proof, aspirational narratives
Implication: When LLMs do recommend products, they can make those recommendations significantly more compelling than human-written copy.
5. The Trust Paradox (GenAI Advertising Research)
Status: 🔬 Research (arXiv:2409.15436)
User study findings that explain why in-response ads aren’t widely deployed:
┌─────────────────────────────────────────────────────────────┐
│ The Trust Paradox │
├─────────────────────────────────────────────────────────────┤
│ │
│ FINDING 1: Users can't detect embedded ads │
│ FINDING 2: Undisclosed ads get higher satisfaction ratings │
│ FINDING 3: Once disclosed, users feel manipulated │
│ FINDING 4: Trust decreases significantly after disclosure │
│ │
│ → No easy win. Hidden ads = unethical. │
│ → Disclosed ads = lower satisfaction. │
│ → Need a third path: genuinely helpful recommendations. │
│ │
└─────────────────────────────────────────────────────────────┘Why this matters: This explains the current production landscape—companies opt for clearly separated ads (Perplexity’s follow-up questions, Google’s sponsored sources) rather than embedded ads because the trust cost is too high.
Summary: Production vs. Research
| Format | Status | Company/Paper |
|---|---|---|
| Sponsored follow-up questions | ✅ Production | Perplexity |
| Sponsored sources/citations | ✅ Production | Google AI Overviews |
| Side panel ads | ✅ Production | Microsoft Copilot |
| Affiliate links | ✅ Production | Various |
| Brand plugin partnerships | ⚠️ Limited | OpenAI (deprecated plugins) |
| In-response ad injection | 🔬 Research | GEM-Bench (arXiv:2509.14221) |
| Commercial intent retrieval | 🔬→✅ Research→Prod | RARE (arXiv:2504.01304) |
| Generative Engine Optimization | 🔬 Research | Rewrite-to-Rank (arXiv:2507.21099) |
| LLM-generated persuasive ads | 🔬 Research | arXiv:2512.03373 |
| Trust/disclosure dynamics | 🔬 Research | GenAI Advertising (arXiv:2409.15436) |
The Trend
Production systems favor separation:
- Ads clearly labeled and visually distinct
- User choice to engage or ignore
- Leveraging existing ad infrastructure
Research explores integration:
- Ads woven into response text
- Personalized and contextual
- New retrieval and generation mechanisms
The gap between research and production reflects the trust problem: users accept clearly-labeled ads but feel manipulated by hidden ones. The winning format will likely be one that’s both integrated AND transparent.
References
Core Papers
- GEM-Bench: A Benchmark for Ad-Injected Response Generation (arXiv:2509.14221) - First comprehensive benchmark for evaluating ad injection in LLM responses
- RARE: Real-time Ad Retrieval via LLM-generative Commercial Intention (arXiv:2504.01304) - Production-validated approach for commercial intent-based ad retrieval
- GenAI Advertising: Risks of Personalizing Ads with LLMs (arXiv:2409.15436) - User study on trust and manipulation perception
Additional Research
- Rewrite-to-Rank: Optimizing Ad Visibility in LLM Retrieval (arXiv:2507.21099) - Generative Engine Optimization techniques
- LLM-Generated Ads: From Personalization Parity to Persuasion Superiority (arXiv:2512.03373) - LLM capabilities in ad content generation