Did you know that around 52% of US adults now use generative AI platforms as their primary tool for online searches?¹
How do you search for information on the Internet? Do you Google it, or jump straight to shooting prompts at ChatGPT, Gemini, or Perplexity or even using Google’s own AI Overviews? Search engine results give you 10 blue links on a page to carry on your quest. However, generative AI search provides a comprehensive one-shot answer to your question that you can further refine with additional prompts. Google is still the most widely used search engine, but with generative AI platforms and even AI Overviews, search has become increasingly AI-driven. They provide a conversational and context-aware search experience. So, the question is, does your brand appear in the generative AI results?
How to Make Your Brand Visible in Gen AI Search?
As a marketer seeking a first-mover advantage, the question of how to optimize content for generative AI visibility arises. How do we show up in generative AI’s results across different user intents, whether they’re creating, researching, or simply trying to understand a topic? Because when brands show up in these channels, they gain value and mindshare. Interestingly, LLMs rely on their training data and use Google or Bing to validate their answers. So, when it comes to optimizing for generative AI search, the aim is to create content that integrates into AI-generated responses, providing answers that match what users need.
Understanding the Three Types of Generative AI Engines
1. Training-First Systems (Claude, Llama)
These systems generate responses exclusively from their training data, which remains static until the model is updated. Influencing these systems requires a long-term strategy, consistently enhancing your digital presence, generating PR momentum, and anticipating future training data updates.
In this context, success relies in establishing a robust, enduring foundation rooted in brand belief systems. This approach underscores the importance of thought leadership pieces, case studies, and authoritative content that gradually solidify brand authority and credibility.
2. Search-First Systems (Perplexity, AI Overviews)
These generative AI engines practice real-time web page indexing. Here, you will find the brand influence is more immediate. You can optimize your content, maximize your search engine visibility, and make sure the content is among the referenced sources. It is like an accelerated version of SEO and strategic content curation.
A recent survey showed that 60% of consumers now rely on AI for product recommendations, which is more than double what it was two years ago.²
Fundamental SEO principles are key here. To appear in search results promptly, you can simply focus on refining the SEO strategies and securing search engine indexing.
For example, I asked Perplexity: What are the best semiconductor marketing companies? And here are the results. Anion Marketing came in at No. 5 in these search results.
3. Hybrid Systems (ChatGPT, Gemini)
These engines work dynamically, often determining in real-time whether to fetch the latest information from the web or answer based on the training data. For instance, if we ask a question on the basics of investing, it might rely on its training data. However, if we enquire about a piece of breaking news, it will run real-time search and pull out the details. What triggers the real-time search or the reliance on old training data is the prompt.
The key is to strike a balance here. It is critical to have a strong foundational content base while being agile and keeping up with real-time updates that ensure your brand remains relevant. Latest data reveals that session times, conversational query length, and breadth of prompt types are higher in generative search than traditional search. This shift means brands need to appear not just in static answers, but within multi-step conversational flows.
I asked the same question in ChatGPT: What are the best semiconductor marketing companies? It is a general question, so it relied on its training data. On further prompting, ChatGPT did a real-time search and voila! Our brand ranked No. 2!
Testing and Learning Across AI Models
As a marketer, you can experiment with the various AI systems to assess content visibility and ranking patterns, both across and within models. By monitoring the changes and noting the attributes that consistently appear for your queries, which ones vary and, to what extent, you can decide the strategies for each of the three AI systems.
Strategies for the Different Types of Generative AI Engines
Let us take a closer look at each of the Gen AI systems and find out the best strategy.
Optimizing for Training-First AI Systems like Claude or Llama:
The strategy here is to build a strong digital presence that is consistent, valuable, and authoritative. Being a long-term strategy, this approach may not reflect immediate results, but when these AI systems update their training data, ensure that your brand becomes part of their knowledge base.
- Action: Focus on creating high-quality, evergreen content that ensures brand positioning as an industry leader. You can also identify gaps in search intent, where the user may be looking for alternative options. These gaps can form a strategic starting point.
- Breaking the silo: Advertising, PR, and initiating long-term reputation-building initiatives strengthen the brand’s image in the public consciousness. The outlook is long-term.
Optimizing for Search-First Systems like Perplexity:
These Gen AI systems work in real-time, pulling information from web indexes to generate answers. To influence Search-First Systems, the key lies in understanding how these Gen AI systems process and prioritize content. They are highly responsive to SEO tactics, making search optimization a direct path to Generative AI Optimization.
Steps that marketers can take:
- Use proven SEO strategies, ensuring your content is relevant and well-optimized for discoverability
- Publish timely, newsworthy content that aligns with user interests
- Prioritize indexability and aim for a strong indexing reputation
Optimizing for Hybrid Systems like ChatGPT or Gemini:
The Hybrid AI systems constantly switch between training data and retrieving real-time information. For these versatile systems, marketers, too, need to be agile.
Will only optimizing SEO do the trick here? No. Marketers must understand how different queries are processed in these popular Hybrid AI systems. To separate the customers’ potential queries that will trigger ChatGPT to fetch answers in real-time from the web vs. the ones ChatGPT answers trusting its legacy training data. Prompts that trigger real-time search are the key to visibility here.
How Should Marketers Strategize for This?
- Evergreen content:
Ensuring your content is evergreen and robust enough for the Hybrid Gen AI model to refer to from training data. - Refreshing content cadence:
Evergreen and robust content also goes stale. Monitoring, refreshing, and making it relevant to today’s context is imperative. - Being agile:
Check insights, keep an eye on emerging trends and be ready to jump on addressing trending topics with up-to-date content.
Which Path to Take?
Is there a one-size-fits-all approach here? No!
Understanding how each AI system works and how your customers search for answers is the key. It may feel like entering uncharted territory. However, for a marketer, the fundamentals remain the same: understanding the target audience, meeting them where they are, being curious, being relevant, providing value, and always evolving. Whether you are building a lasting legacy with Training-First AI Systems, honing the fast-paced race with Search-First Systems, or balancing both with Hybrid Systems, the key lies in agility.
So, which path do you want to take, or do you want to master them all? With an AI-driven future, it is okay to feel overwhelmed. We, at Anion Marketing can help you to strategize and reach your customers exactly where they are. We can craft a tailored strategy for you. Contact Anion Marketing today!
¹ Source: Elon University
² Source: Omnisend
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About Author
Senior Content Writer at Anion Marketing, translating complex semiconductor and tech concepts into impactful B2B content. Backed by 10 years’ experience and a MICA certification in Digital Marketing.
Did you know that around 52% of US adults now use generative AI platforms as their primary tool for online searches?¹
How do you search for information on the Internet? Do you Google it, or jump straight to shooting prompts at ChatGPT, Gemini or Perplexity or even using Google’s own AI overviews? Search engine results give you 10 blue links on a page to carry on your quest. However, generative AI search provides a comprehensive one-shot answer to your question that you can further refine with additional prompts. Google is still the most widely used search engine, but with generative AI platforms and even AI overviews, search has become increasingly AI-driven. They provide a conversational and context-aware search experience. So, the question is, does your brand appear in the generative AI results?
How to Make Your Brand Visible in Gen AI Search?
As a marketer seeking a first-mover advantage, the question of how to optimize content for generative AI visibility arises. How do we show up in generative AI’s results across different user intents, whether they’re creating, researching, or simply trying to understand a topic? Because when brands show up in these channels, they gain value and mindshare. Interestingly, LLMs rely on their training data and use Google or Bing to validate their answers. So, when it comes to optimizing for generative AI search, the aim is to create content that integrates into AI-generated responses, providing answers that match what users need.
Coming to generative AI searches, there are several engines, but all of these can be boiled down to three types (which I’ll explain in the next section). So, marketers must clearly define the objectives of their optimization efforts and determine the strategy for each type of AI search engine. Now that we recognize Gen AI optimization is paramount, search marketers should align their strategies with the generative engines they target. Tailored strategies are a game-changer for real, impactful results.
Understanding the Three Types of Generative AI Engines
1. Training-First Systems (Claude, Llama)
These systems generate responses exclusively from their training data, which remains static until the model is updated. Influencing these systems requires a long-term strategy, consistently enhancing your digital presence, generating PR momentum, and anticipating future training data updates.
In this context, success relies in establishing a robust, enduring foundation rooted in brand belief systems. This approach underscores the importance of thought leadership pieces, case studies, and authoritative content that gradually solidify brand authority and credibility.
2. Search-First Systems (Perplexity, AI Overviews)
These generative AI engines practice real-time web page indexing. Here, you will find the brand influence is more immediate. You can optimize your content, maximize your search engine visibility, and make sure the content is among the referenced sources. It is like an accelerated version of SEO and strategic content curation.
A recent survey showed that 60% of consumers now rely on AI for product recommendations, which is more than double what it was two years ago.²
Fundamental SEO principles are key here. To appear in search results promptly, you can simply focus on refining the SEO strategies and securing search engine indexing.
For example, I asked Perplexity: "What are the best semiconductor marketing companies?" And here are the results. Anion Marketing came in at No. 5 in these search results.
3. Hybrid Systems (ChatGPT, Gemini)
These engines work dynamically, often determining in real-time whether to fetch the latest information from the web or answer based on the training data. For instance, if we ask a question on the basics of investing, it might rely on its training data. However, if we enquire about a piece of breaking news, it will run real-time search and pull out the details. What triggers the real-time search or the reliance on old training data is the prompt.
The key is to strike a balance here. It is critical to have a strong foundational content base while being agile and keeping up with real-time updates that ensure your brand remains relevant. Latest data reveals that session times, conversational query length, and breadth of prompt types are higher in generative search than traditional search. This shift means brands need to appear not just in static answers, but within multi-step conversational flows.
I asked the same question in ChatGPT: What are the best semiconductor marketing companies? It is a general question, so it relied on its training data. On further prompting, ChatGPT did a real-time search and voila! Our brand ranked No. 2!
Testing and Learning Across AI Models
As a marketer, you can experiment with the various AI systems to assess content visibility and ranking patterns, both across and within models. By monitoring the changes and noting the attributes that consistently appear for your queries, which ones vary and, to what extent, you can decide the strategies for each of the three AI systems.
Strategies for the Different Types of Generative AI Engines
Let us take a closer look at each of the Gen AI systems and find out the best strategy.
Optimizing for Training-First AI Systems like Claude or Llama:
The strategy here is to build a strong digital presence that is consistent, valuable, and authoritative. Being a long-term strategy, this approach may not reflect immediate results, but when these AI systems update their training data, ensure that your brand becomes part of their knowledge base.
- Action: Focus on creating high-quality, evergreen content that ensures brand positioning as an industry leader. You can also identify gaps in search intent, where the user may be looking for alternative options. These gaps can form a strategic starting point.
- Breaking the silo: Advertising, PR, and initiating long-term reputation-building initiatives strengthen the brand’s image in the public consciousness. The outlook is long-term.
Optimizing for Search-First Systems like Perplexity:
These Gen AI systems work in real-time, pulling information from web indexes to generate answers. To influence Search-First Systems, the key lies in understanding how these Gen AI systems process and prioritize content. They are highly responsive to SEO tactics, making search optimization a direct path to Generative AI Optimization.
Steps that marketers can take:
- Use proven SEO strategies, ensuring your content is relevant and well-optimized for discoverability
- Publish timely, newsworthy content that aligns with user interests
- Prioritize indexability and aim for a strong indexing reputation
Optimizing for Hybrid Systems like ChatGPT or Gemini:
The Hybrid AI systems constantly switch between training data and retrieving real-time information. For these versatile systems, marketers, too, need to be agile.
Will only optimizing SEO do the trick here? No. Marketers must understand how different queries are processed in these popular Hybrid AI systems. To separate the customers’ potential queries that will trigger ChatGPT to fetch answers in real-time from the web vs. the ones ChatGPT answers trusting its legacy training data. Prompts that trigger real-time search are the key to visibility here.
How Should Marketers Strategize for This?
- Evergreen content: Ensuring your content is evergreen and robust enough for the Hybrid Gen AI model to refer to from training data.
- Refreshing content cadence: Evergreen and robust content also goes stale. Monitoring, refreshing, and making it relevant to today’s context is imperative.
- Being agile: Check insights, keep an eye on emerging trends and be ready to jump on addressing trending topics with up-to-date content.
Which Path to Take?
Is there a one-size-fits-all approach here? No!
Understanding how each AI system works and how your customers search for answers is the key. It may feel like entering uncharted territory. However, for a marketer, the fundamentals remain the same: understanding the target audience, meeting them where they are, being curious, being relevant, providing value, and always evolving. Whether you are building a lasting legacy with Training-First AI Systems, honing the fast-paced race with Search-First Systems, or balancing both with Hybrid Systems, the key lies in agility.
So, which path do you want to take, or do you want to master them all? With an AI-driven future, it is okay to feel overwhelmed. We, at Anion Marketing can help you to strategize and reach your customers exactly where they are. We can craft a tailored strategy for you. Contact Anion Marketing today!
¹ Source: Elon University
² Source: Omnisend
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