Optimisation SEO sur les LLMs – Comment devenir visible sur les nouveaux moteurs de recherche

As ChatGPT, Perplexity, Gemini, and other large language models (LLMs) gain popularity, brands have one fear: disappearing de moteurs de recherche. By providing answers directly à users, these generative AIs are disrupting the SEO stratégies of many companies. 

But to respond to users with complete transparency, LLMs must provide their sources. And that’s where l'IA générative optimisation comes in. With this new generation of natural referencing, you can devenir visible on any propulsé par l'IA moteurs de recherche. Let’s take a closer look. 

Exploring l'IA générative and large language models

Why understanding TAL and LLM is crucial:

Becoming familiar with traitement du langage naturel (NLP) and large language models (LLMs) is an essential step in anticipating developments in moteurs de recherche optimisation, digital branding and content stratégies. A thorough understanding of these technologies allows their full potential to be exploited.

The ideas presented Voici based on a decade of work in semantic research, in-depth studies of scientific literature, and analysis of patents related to l'IA générative.

How large language models work

The basics of LLMs

Before adopting tools such as GEO (Generative Engine optimisation), it is essential to understand the technological foundations of LLMs. Just as it is essential to master moteurs de recherche to avoid ineffective practices, investing a few hours in learning these concepts can save time and resources by ruling out irrelevant stratégies.

LLMs: a revolutionary advance

Language models such as GPT, Claude, and LLaMA represent a major transformation in the way l'IA générative and moteurs de recherche process queries.

They do not simply search for textual matches, but generate nuanced and contextually rich responses, thanks to their advanced linguistic comprehension and reasoning capabilities. Par exemple, research such as Microsoft’s « Large Search Model: Redefining Search Stack in the Era of LLMs » highlights their role in reshaping search technologies.

Advanced LLM features in search

LLMs adopt a unified approach to treating all research-related tasks as text generation problems. This enables them to:

• Generate comprehensive responses: They produce summaries in natural language, going beyond simple information extraction.
• Customise responses: Thanks to specific prompts, templates adapt their results to users’ needs.
• Improving queries: In cases of insufficient data, LLMs generate additional queries to collect more relevant information.

Key stages of treatment with LLM

Encoding: structuring data

Raw data is transformed into tokens, the fundamental units of models. These tokens represent various types of information (words, entities, images, etc.) depending on the application.

Transforming tokens into vectors

Tokens are converted into vectors, an essential step in transformer-based models such as Google’s technology. These vectors, which are numerical representations of tokens, capture their specific attributes and enable semantic relationships to be measured using methods such as cosine similarity or Euclidean distance.

This vector- and transformer-based approach revolutionised l'IA générative and remains a key factor in the widespread adoption of LLMs today.

The decoding process

Decoding is the stage where the model interprets the probabilities associated with each possible next token (word or symbol). The aim is to produce a fluid and natural sequence.

Decoding techniques

To achieve this objective, several methods are used:

• Top K sampling: Considers the K most probable words as options pour le next step.
• Top P sampling: Takes into account words whose cumulative probabilities reach a certain threshold (P), permettant for greater variation.

These methods influence the model’s level of creativity. Par exemple, a « strict » model favours the most probable options, producing consistent and predictable responses, while a « more flexible » model explores alternatives, generating varied responses.

Diversity of outputs and creativity

The choice of decoding method explains why the same prompt can produce different results. With greater creative freedom, models consider a wider range of potential words, which encourages more original and nuanced responses.

Beyond text: the multimedia capabilities of l'IA générative

Bien que encoding and decoding processes are mainly associated with text processing, they also apply to other formats such as audio and visuals. These contents are first converted into text tokens before being processed by the models.

For GEO applications, this multimedia capability is less relevant. Cependant, the expanded context window of LLMs provides a better understanding of the relationships between primary and secondary entities in sentences, thereby enriching the results produced.

Défis and solutions of l'IA générative

Large language models (LLMs) face three main challenges:

• Updating information: Preventing answers from becoming obsolete.
• Reducing hallucinations: Ensuring the accuracy of responses.
• Thematic accuracy: Provide detailed information on specific topics, rather than generic answers.

To remedy this, recovery-augmented generation (RAG) is emerging as an effective solution.

Augmented Generation through Recovery (AGR): How it Works and Its Avantages

RAG enriches LLMs by providing them with subject-specific data, integrated in the form of documents or vectors representing complex semantic relationships (as in knowledge graphs).

This approach enables:

• A better understanding of the relationships between entities.
• Greater precision in thematic responses.

Applications and challenges for GEO

RAG provides opportunities for tools such as GEO by facilitating access to relevant sources and permettant customisation of the data used.

Cependant, the challenge remains in determining how platforms select and evaluate the quality of sources, a key factor in ensuring the relevance and reliability of responses.

l'importance de recovery models in RAG architecture

Retrieval models play a fundamental role in retrieval-augmented generation (RAG) systems, acting as « specialised librarians » capable of identifying relevant information in huge data sets.

Operation and benefits

These models use advanced algorithms to evaluate and select the most useful data, incorporating external knowledge into text generation. This enables:

• Enriched and contextualised content production.
• An extension of the capabilities of classical language models.

Recovery systems rely on several technologies, including:

• Vector incorporations and vector search.
• Indexing documents using techniques such as BM25 or TF-IDF.

Variations and limitations of recovery approaches

Not all AI systems incorporate sophisticated retrieval mechanisms, which poses challenges for optimising RAG architectures.

• Meta: Develops its own moteurs de recherche for its LLaMA models, aiming for better control over the data retrieved.
• Perplexity: Uses proprietary indexing and ranking systems, Bien que it is sometimes accused of relying on results from engines such as Google.
• Claude (Anthropic): Its use of RAG remains somewhat opaque, potentially combining user data and its own index.
• Gemini, Copilot, and ChatGPT: Utilise their partners’ moteurs de recherche (Microsoft Bing for ChatGPT) or develop proprietary solutions such as SearchGPT.

The case of ChatGPT and SearchGPT

OpenAI recently a présenté SearchGPT, combining several technologies to enrich its responses:

• A model based on a refined version of GPT-4, trained with synthetic data distillation techniques.
• Partnerships with third-party search providers (including Microsoft Bing) and direct content from partners.

Bien que ChatGPT shows similarities with Bing’s rankings for certain queries, its model uses diverse sources, ensuring a certain degree of independence in its responses.

RAG pipeline assessment: key criteria

The evaluation of a recovery-augmented generation (RAG) system relies on several key metrics pour mesurer its performance and reliability.

Key assessment criteria

• Accuracy: Checks the factual consistency of responses in relation to the context provided.
• Relevance of the response: Measures how well the generated response matches the prompt.
• Context accuracy: Evaluates the correct classification of relevant contextual elements, with scores ranging de 0 à 1.
• Anchoring: Verifies whether responses are directly supported by source information, ensuring their verifiability.
• Source references: Ensures the presence of citations and links to facilitate verification.
• Distribution and coverage: Ensures balanced representation among the various sources and sections consulted.
• Accuracy of facts: Checks pour le presence of accurate factual information in the generated content.
• Custom criteria (aspect-based review): Enables evaluation based on specific aspects, such as accuracy or safety.

Specific performance indicators

• Average Precision (MAP): Average of the precisions calculated for each query, taking into account the ranking of documents in the results. A high MAP reflects better relevance of résultats de recherche.
• Mean reciprocal rank (MRR): Measures how quickly the first relevant document appears in the results. A high MRR indicates that the most relevant results are ranked highly.
• Autonomous quality: A score de 1 à 5 that assesses whether the generated content is understandable without external context.

Difference between prompts and queries

• Queries: Often mots-clés or short phrases, they target specific results in a classic search.
• Prompts: Formulated in natural language, they provide more detailed context and allow for more nuanced responses.

In RAG systems, the prompt is transformed into a background query to efficiently query databases while preserving the original context, improving the accuracy and relevance of responses.

optimisation of AI previews and AI assistants

• AI snippets (Google SERP): Generated from requêtes de recherche, they provide direct extracts in response.
• AI assistants: Based on complex prompts, they offer richer, more contextualised responses.

To bridge the gap between the two, RAG adapts complex prompts into queries while preserving critical context, ensuring optimal retrieval of relevant sources.

GEO objectives and stratégies

The objectives of GEO (Global Entity optimisation) vary Selon the ambitions of the stakeholders:

• Being cited in source links: This stratégie aims to include content directly referenced in sources cited by l'IA générative systems.
• Obtain direct mentions: This involves increasing the likelihood that your brand, product, or name will be included in AI-generated responses.

Bien que these objectives are complementary, they require distinct approaches. In both cases, it is essential to establish a strong presence among the sources favoured or frequently consulted by language models.

Prioritisation of linguistic models

Each AI assistant uses specific criteria to select and recommend entities. This involves:

1. Understanding LLM mechanisms: Studying the differences between large language models such as Gemini, Copilot, ChatGPT, and Perplexity.
2. Monitor emerging markets: Identify applications that dominate specific sectors or regions.

For professionals accustomed to focusing on Google, this requires a strategic shift, incorporating a variety of platforms and constantly monitoring market developments.

Mentions and citations in generated responses

AI systems use contextual attributes to structure their responses. Par exemple:

Example prompt:
« I am 47 years old, weigh 95 kg, am 180 cm tall and run 3 times a week for 6 to 8 kilometres. quelles sont les best jogging shoes for me? »

• Contextual attributes: age, weight, height, running frequency and distance.
• Main entity: jogging shoes.

Products or services frequently associated with these contexts are more likely to be mentioned by AI systems.

insights into source selection

Platforms such as ChatGPT and Perplexity demonstrate how AI systems identify and cite content based on queries:

• titres such as « The best shoes for heavy runners en 2024 » or « The 7 best long-distance running shoes » show that AI deduces specific information from user attributes.

Working upstream to align votre contenu with AI assistant criteria is Par conséquent crucial to maximiser visibility and mentions in the responses generated.

Co-pilot 

Copilot analyses attributes such as age and weight to contextualise responses.

Based on the data provided, he can deduce a context of overweight by referring to the sources cited.

The sources cited come exclusively from informative content, such as tests, reviews and rankings, rather than e-commerce pages or detailed product descriptions.

ChatGPT

ChatGPT takes into account attributes such as distance travelled and weight. Based on the referenced sources, it deduces a context of being overweight and long-distance running.

The sources cited come exclusively from informative content, such as tests, reviews and rankings, rather than from e-commerce pages, such as product descriptions or categories.

Perplexity

Perplexity takes into account the weight attribute and deduces a context of overweight from the referenced sources.

Sources include informative content such as tests, reviews, rankings, ainsi que traditional e-commerce pages.

Gemini

Gemini does not directly provide sources in its results. Cependant, further analysis shows that it also takes age and weight contexts into account.

Gemini release – invitation to choose the best running shoes Gemini
sources – invitation to choose the best running shoes

Each major LLM recommends different products, but one shoe is consistently suggested by all four AI systems tested.

Each major LLM offers different products, but one shoe is consistently recommended by all four AI systems tested.

All systems show some creativity, suggesting different products during various sessions. Copilot, Perplexity, and ChatGPT favour non-commercial sources, such as review sites or test pages, in line with the prompt’s objective.

Bien que Claude offers shoe models, it relies solely on its initial training data, without access to en temps réel data or a retrieval system.

Each LLM has its own process for selecting sources and content, making the GEO challenge more complex. Recommendations are influenced by co-occurrence frequency and context, which increases the likelihood of certain tokens during decoding.

Selection of sources and information for augmented generation through recovery

GEO emphasises the strategic positioning of products, brands, and content in the datasets used to train LLMs. Understanding the training process of these models in detail is crucial for identifying intégration opportunities.

The information presented below is drawn from studies, patents, scientific publications, EEAT research, and personal analysis. The main questions raised are:

• Qu'est-ce que the influence of retrieval systems in the RAG (Retrieval-Augmented Generation) process?
• Qu'est-ce que le rôle de initial training data?
• What other factors may influence the results?

Recent work, particularly on the selection of sources for tools such as AI Overviews, Perplexity, and Copilot, highlights significativement overlaps in the sources used.

Par exemple, analyses conducted by Rich Sanger, Authoritas, and Surfer show that the snippets generated by Google AI display approximately 50% overlap in their selection of sources.

Analysis of fluctuation margins and l'impact de recovery systems

The margin of fluctuation in study overlaps remains significativement. At the beginning of 2024, the overlap rate stood at around 15%, Bien que it reached up to 99% in some analyses.

Recovery systems appear to influence nearly 50% of the results of AI-generated previews, indicating ongoing experiments aimed at optimising performance. These observations corroborate criticisms about the variable quality of the responses produced by these systems.

The selection of sources used in AI responses reveals strategic opportunities to position brands or products in a contextually relevant manner. Cependant, it is crucial to distinguish between:

1. The data sources used pour le initial training of models, which shape their basic understanding.
2. Sources added for specific topics, integrated during the retrieval-augmented generation (RAG) process.

Examining the model training process sheds light on these distinctions. Par exemple, Gemini, Google’s large multimodal model, processes diverse data such as text, images, audio, video, and code. It uses web documents, books, code, and multimedia content for training, permettant it to handle complex tasks efficiently.

Finally, analyses of AI insights and the most frequently referenced sources offer valuable insights into the indices and Knowledge Graph used by Google during model pre-training. This paves the way for alignment stratégies to include relevant content.

In the RAG process, domain-specific sources are integrated to enhance the contextual relevance of responses.

A key feature of Gemini is its use of a mixture of experts (MoE) architecture. Unlike traditional transformers, which rely on a single neural network, a MoE model is composed of smaller, specialised « expert » networks. This model selectively activates the most relevant expert paths based on the input data, optimising the efficiency and performance of the system. It is likely that the RAG process will be integrated into this architecture.

Developed by Google, Gemini undergoes several training stages, using publicly available data and advanced techniques to maximiser the relevance and accuracy of the content generated:

Pre-training
Like other large language models (LLMs), Gemini is first pre-trained on a variety of public data sources. Google applies various filters to ensure data quality and avoid undesirable content. This phase also involves flexible selection of probable words, permettant the model to generate more creative and contextually appropriate responses.

Supervised fine-tuning (SFT)
After pre-training, the model is refined using high-quality examples, either created by experts or generated by other models and then reviewed by specialists. This process is similar to learning good textual structure by observing examples of well-written texts.

Reinforcement learning from human feedback (RLHF)
The model is then refined through human evaluations. A reward system based on user preferences helps Gemini recognise and learn preferred response styles and content types.

Extensions and augmentation through retrieval
Gemini can search external data sources, such as Google Search, Maps, YouTube, or other specific extensions, to enrich responses with up-to-date contextual information. Par exemple, to answer questions about the weather or current events, Gemini can query Google Search directly to find reliable, recent information and then incorporate it into its response.

The model filters résultats de recherche to include only the most relevant information based on the context of the query. Par exemple, for a technical question, it will prioritise scientific or technical results over general information available on the web.

Gemini combines the retrieved data with its internal knowledge to generate an optimised response. This process includes creating a logically structured and readable response, followed by a final review to ensure that it meets Google’s quality standards and does not contain inappropriate content. This quality control is reinforced by a ranking system that prioritises the most relevant responses. The model then presents the highest-ranked version to the user.

Some studies suggest that a high ranking in moteurs de recherche increases the likelihood that a source will be cited in AI applications connected to them. Cependant, as analyses have shown, current overlaps do not yet reveal a clear link between the highest rankings and the sources used.

Another criterion seems to influence the choice of sources: Google’s approach favours compliance with quality standards when selecting sources for pre-training and the RAG process. en plus, the use of classifiers is also cited as a determining factor in this process.

When naming classifiers, a link can be established with the concept of EEAT, where quality classifiers also play a key role.

The information provided by Google on post-training also mentions the use of EEAT to classify sources Selon their quality.

The reference to assessors refers to le rôle de quality assessors in the EEAT assessment.

Rankings in most moteurs de recherche depend on the relevance and quality of information, both in terms of the document, the domain, and the author or source entity.

Sources are often selected non seulement for their relevance, mais aussi for their quality in terms of the subject area and the source entity.

This can be explained by the fact that more complex queries must be rewritten in the background to generate appropriate requêtes de recherche for querying the rankings. Bien que relevance varies depending on the query, quality remains a constant criterion.

This distinction helps to understand the weak correlation between moteurs de recherche rankings and sources referenced by l'IA générative, and why lower-ranked sources may sometimes be included.

To evaluate quality, moteurs de recherche such as Google and Bing use classifiers, including Google’s EEAT framework. Google specifies that EEAT can vary depending on the domain, thus requiring stratégies tailored to each subject, particularly in GEO stratégies.

The sources used differ depending on the sector or subject, with platforms such as Wikipedia, Reddit and Amazon playing various roles, as indicated by a study by BrightEdge.

Par conséquent, industry- and subject-specific factors must be taken into account in positioning stratégies.

Tactical and strategic approaches for LLMO/GEO

As mentioned earlier, there is still no tangible evidence regarding the direct influence on the results of l'IA générative. Platform operators themselves seem uncertain about comment qualify the sources chosen during the RAG process.

These factors highlight l'importance de identifying areas where optimisation efforts should be focused, particularly by determining which sources are sufficiently reliable and relevant to be prioritised.

The next challenge is to understand comment position yourself as one of these authoritative sources.

The research paper entitled « GEO: Generative Engine optimisation » a présenté the concept of GEO, exploring how l'IA générative outcomes can be influenced and identifying the key factors responsible for this influence.

Selon the study, the visibility and effectiveness of the GEO can be optimised by the following elements:

1. Authority in writing: It improves performance, especially on debate questions or queries in historical contexts. More persuasive writing is indeed more valuable in these situations where argumentation plays a central role.
2. Quotations (source references): These are particularly useful for factual questions, as they provide a source for verifying information, thereby reinforcing the credibility of the generated response.
3. Statistical addition: Highly effective in fields such as law, government, or public opinion, incorporating relevant statistics into web page content can improve visibility in specific contexts.
4. Direct quotations: These have a more significativement impact in fields such as social sciences, history or personal narratives, as they add authenticity and depth to responses.

These factors vary depending on the domain, suggesting that integrating targeted, sector-specific personalisation is essential for increasing the visibility of web pages.

The following tactical measures for GEO and LLMO can be derived from this document:

1. Use citable sources: Incorporate reliable and verifiable sources into votre contenu to reinforce credibility and authenticity, especially for factual information.
2. Add statistics: Include relevant statistics to support your arguments, particularly in areas such as law, government, or opinion issues.
3. Use quotations: Enrich content with quotations in fields such as social sciences, history, or explanations, as they add depth and authenticity.
4. Domain-specific optimisation: Take into account the specific characteristics of your sector when optimising, as the effectiveness of GEO techniques can vary depending on the domain.
5. Focus on content quality: Create high-quality, relevant, and informative content that meets the real needs of users.

En outre, certain tactical practices should be avoided:

1. Avoid mot-clé stuffing: Traditional mot-clé stuffing does not generally lead to significativement improvements in AI-generated results and should be avoided.
2. Do not ignore the context: Avoid generating content that is unrelated to the subject or that does not add value pour le user.
3. Do not overlook user intent: Ensure that votre contenu truly meets users’ expectations and needs based on their requêtes de recherche.

Selon BrightEdge’s research, Voici the strategic considerations to take into account:

Different impacts of backlinks and co-citations

AI and Perplexity insights favour distinct sets of domains depending on the sector.
In the fields of health and education, these platforms favour reliable sources such as mayoclinic.org and coursera.com, making them key targets for effective SEO stratégies.
In contrast, in the e-commerce and finance sectors, Perplexity favours sites such as reddit.com, yahoo.com, and marketwatch.com. Tailoring your SEO efforts to these preferences using backlinks and co-citations can significativement improve performance.

Tailor-made stratégies for basées sur l'IA research

basées sur l'IA search stratégies must be tailored to each sector.
Par exemple, Perplexity’s preference for reddit.com shows l'importance de community information in e-commerce, while AI Overviews favours sites such as consumerreports.org and quora.com for reviews and Q&As.
Marketers and SEO specialists should align their content stratégies with these trends by creating detailed product reviews or supporting Q&A forums for e-commerce brands.

Anticipating changes in the citation landscape

SEO specialists must closely monitor Perplexity’s preferred sites, particularly the growing influence of reddit.com for community content.
Google’s partnership with Reddit could influence Perplexity’s algorithms, giving it greater priority.
SEO specialists must remain proactive and adjust their stratégies to adapt to changes in citation preferences, ensuring the relevance and effectiveness of their actions.

B2B technology

• Establish a presence on authoritative sites web: Publish on reputable domains such as techtarget.com, ibm.com, microsoft.com, and cloudflare.com, which are perceived as reliable sources.
• tirer parti de content syndication: Take advantage of established platforms to quickly increase your citation as a reliable source.
• Build your domain authority: In the long term, produce quality content to establish lasting authority, as competition for syndication spots will increase.
• Partnerships with key platforms: Actively collaborate with industry leaders and contribute to their content.
• Demonstrate your expertise: Display certifications, qualifications, and expert testimonials to signal your reliability.

E-commerce

• Presence on Amazon: Focus on Amazon, which is a preferred source for Perplexity.
• Promote product reviews: Highlight product reviews and user-generated content on Amazon and other relevant platforms.
• Distributors and comparison sites: Distribute product information on established reseller platforms and comparison sites.
• Syndication and partnerships: Syndicate content on reliable domains and establish partnerships with trusted sites.
• Regularly update product information: Ensure that product descriptions are detailed and up to date on all sales platforms.
• Get involved in community forums: Be active on platforms such as Reddit and other specialist portals.
• Balanced marketing stratégie: Combine a strong presence on external platforms with the development of your own domain authority.

Continuing education

• Collaboration with authoritative sites web: Work with sites web such as coursera.org, usnews.com, and bestcolleges.com to create reliable sources.
• High-quality, up-to-date content: Create well-structured, up-to-date content based on specialist knowledge.
• Presence on community platforms: Be active on Reddit and other relevant forums, as community content is becoming essential.
• Optimising votre contenu: Use a clear structure and concise headings pour optimiser votre contenu for AI systems.
• Highlight certifications and accreditations: They reinforce the credibility and reliability of votre contenu.

Finance

• Presence on reliable financial portals: Use platforms such as yahoo.com and marketwatch.com, which are preferred sources for AI.
• Keeping information up to date: Ensure that financial information is accurate and current on major platforms.
• High-quality, factual content: Create verifiable content backed by recognised sources.
• Active presence on Reddit: Get involved in relevant financial communities, as Reddit is becoming increasingly popular with AI.
• Partnerships with financial media: Collaborate with established financial media outlets to increase votre visibilité and credibility.
• Demonstrate expertise: Showcase specialist knowledge, certifications, and expert testimonials.

Health

• Link to reliable sources: Include references to sites web such as mayoclinic.org, nih.gov, and medlineplus.gov.
• intégration of medical trends: Regularly update content with les dernières medical research.
• Well-researched content: Provide detailed medical information backed by official institutions.
• Credibility and expertise: Highlight certifications and qualifications to reinforce reliability.
• Regular content updates: Adapt votre contenu in line with new medical discoveries.
• Balanced stratégie: Use a combination of content on established health platforms and strengthening your own domain authority.

Insurance

• Use reliable sources: Publish on reputable sites such as forbes.com and .gov sites to boost your credibility.
• Accuracy of information: Ensure that all information about products and services is accurate and up to date.
• Content syndication: Share votre contenu on platforms such as Forbes to be cited as a reliable source more quickly.
• Local relevance: Tailor votre contenu to local markets and regulations in the insurance sector.

Restaurants

• Presence on review platforms: Maintain a strong presence on Yelp, TripAdvisor, OpenTable, and GrubHub.
• Positive reviews and ratings: Encourage customers to leave positive reviews and interact with them.
• Complete information: Ensure that information about menus, opening hours, and photographs is complete and up to date.
• Engagement with culinary communities: Actively participate in sites such as Eater.com and other food platforms.
• Local SEO optimisation: Perform local SEO, as AI often favours local relevance.
• Wikipedia entries: Maintain comprehensive and well-maintained Wikipedia entries.
• Seamless online booking process: Offer a simple online booking service via relevant platforms.

Tourism / Travel

• optimisation of presence on travel platforms: Create attractive, optimised content for TripAdvisor, Expedia, Kayak, Hotels.com and Booking.com.
• Comprehensive and authentic content: Provide detailed travel guides, tips, and reviews.
• Optimising the booking process: Ensure that the booking process is simple and user-friendly.
• Local SEO: intégrer optimisation for local searches, as AI favours localisation.
• Encourage reviews on platforms: Be active on travel platforms and encourage users to leave reviews.
• Collaborate with trusted partners: Develop partnerships with reputable travel sites web to increase votre visibilité and credibility.

l'avenir de GEO: implications for brands

l'avenir de GEO, particularly in the context of propulsé par l'IA search such as ChatGPT, marks a significativement shift for corporate marketing stratégies. Il s'agit largely based on how consumer search behaviours are evolving and how companies are en tirant parti de new technologies to position themselves in these environments.

Here is a summary of the key points of this development and what it means for brands:

1. l'importance de GEO and l'IA générative

• la montée de ChatGPT and Bing: If ChatGPT becomes a dominant l'IA générative application, ensuring that your brand is well positioned on Microsoft Bing (which powers ChatGPT) could become crucial to influencing AI applications. This development could enable Bing to regain part de marché from Google.

2. Key objectives for businesses:

• Proprietary media as sources for LLM training: Companies must establish a presence in proprietary media by producing content that complies with EEAT (Expertise, Authoritativeness, Trustworthiness) principles. This ensures that the information provided is considered reliable and is used in the LLM training process.
• Generate mentions in reputable media outlets: Being cited in authoritative platforms is essential for strengthening the perception of the brand by AI and ensuring strong co-occurrence of the brand with relevant attributes and entities in these media outlets.
• Producing content optimised for RAG systems: Creating high-quality content that ranks well and is taken into account in RAG (Retrieval-Augmented Generation) systems is another key element of optimisation for AI.

3. Difference between niche markets and large markets

• Niche markets: In these markets, it is easier to position oneself due to less competition. Fewer co-occurrences are needed to associate a brand with specific entities in LLM systems.
• Large markets: In more competitive markets, where brands already have a strong presence, the task becomes more complex. Here, managing public perception on a large scale becomes essential, requiring considerable resources in public relations and marketing.

4. le rôle de traditional moteurs de recherche optimisation

• Bien que GEO or LLMO stratégies require more substantial investments, traditional SEO can still play a key role in training LLM models, provided that high-quality, well-structured and relevant content is produced.

5. Digital management of authority

• The concept of digital authority management is becoming crucial for businesses to se prépare pour the future. This involves structuring efforts in public relations, SEO, and content creation to ensure that the brand is perceived as a reliable and credible source by both users and AI.

6. Avantages for major brands

• Major brands, with their public relations and marketing resources, will likely enjoy a substantial advantage in moteurs de recherche rankings and l'IA générative results. Their ability to generate visibility and reinforce brand perception through mentions in reliable media outlets will enable them to position themselves more effectively.

Conclusion:

Businesses must focus non seulement on traditional SEO, mais aussi on the co-occurrence of their brand with relevant entities and attributes in authoritative media. réussir in a world dominated by l'IA générative, it will be crucial pour optimiser relationships on recognised platforms and invest in digital authority management to influence public perception on a large scale. 😊

Cela signifie that brands will need to adapt their stratégies to ensure they are well positioned in both traditional moteurs de recherche and AI-generated results, while anticipating rapid changes in the digital landscape.

SEO on LLMs: Optimizations to mettre en œuvre

SEO optimisation on LLMs involves making yourself visible on new moteurs de recherche. 

Whether it’s ChatGPT, Perplexity, Microsoft Copilot, or Gemini, every large language model uses a database to provide answers to internet users. With a few exceptions, this database is simply made up of the vast universe of the internet. Autrement dit, LLMs draw their knowledge from the hundreds of thousands of sites web on the web. Among them is undoubtedly yours. 

Some language models use content without specifying the source to the user. Cependant, pour le sake of transparency, more et plus encore of them are citing the source of the information they provide. Certain particularly relevant sites are thus able to make themselves visible on LLM tools. Users who want to En savoir plus can then click on the site link and access its content.

But to do that, you still need to master SEO optimisation on LLMs. 

comment devenir visible on LLM moteurs de recherche? 

découvrez our tips to help you stand out and devenir visible on next-generation moteurs de recherche. 

1 – Understanding artificial intelligence models 

Before embarking on l'IA générative optimisation, it is essential to fully understand the technologies behind LLMs. This will enable you to identify the future potential of SEO. 

These artificial intelligence assistants go far beyond simple text matching. Autrement dit, they do not just extract information from existing documents. Instead, they generate comprehensive, accurate, relevant, nuanced and contextually rich responses.

Il s'agit possible thanks to their linguistic comprehension and reasoning abilities (traitement du langage naturel – NLP). 

Please note, Cependant, that LLMs do not ‘understand’ internet users’ queries in the human sense of the term. They process data statistically. 

2 – Deliver quality content via SEO

In essence, the main principles of SEO remain unchanged. It is always necessary to maintain the technical aspects of your site web, boost its popularity with a netlinking stratégie, and produce quality content. 

But here, the quality requirements are even higher than before. SEO on LLMs does not seek information from sites that all say the same thing, with different turns of phrase. Instead, l'IA générative wants unique and accurate content, with concrete examples, figures, infographics, études de cas, explicit sources, etc.  

Ultimately, the more general votre contenu is, the less likely it is to meet the requirements of l'IA générative optimisation. 

3 – Define an SEO brand stratégie

Concerned about the relevance of their sources, large language models will not promote unknown sites web. They favour sites that already have a certain authority and popularity. This obviously involves a netlinking stratégie, but non seulement that. Nowadays, you need to define a real brand identity: why are you unique? 

Ultimately, l'IA générative optimisation goes even further than traditional SEO. 

Please note that, at present, there is no proven and approved method for optimising l'IA générative. en fait, each model produces different results in response to the same query. It is Par conséquent not a 100% exact science. 

That said, these key principles maximiser your chances of appearing on new moteurs de recherche. 

With Christmas shopping season approaching, SEO optimisation for LLMs is becoming essential. To capture consumers’ attention on platforms such as SearchGPT, brands must adjust their SEO stratégies by incorporating optimisations tailored to l'IA générative. By focusing on content quality, relevant co-occurrences and visibility on reliable sources, businesses will be able pour améliorer their positioning during this crucial period.

Christmas shopping on SearchGPT: SEO optimisations to plan for

For many people, Christmas shopping is not always a pleasant experience. So when an basées sur l'IA tool offers the possibility of providing a list of ideas tailored to a relatively specific request, including the set budget and key information about the recipient, it would be a shame not to take advantage of it. Bien que this practice has not yet been adopted by all suppliers, who still overwhelmingly favour Google’s moteurs de recherche, it remains an increasingly worrying trend for brands, who see a growing interest in appearing among the first options offered by platforms.

For annonceurs, it is Par conséquent important to monitor their own mentions on basées sur l'IA search platforms and mettre en œuvre an effective SEO stratégie to strengthen their online presence. Il s'agit a much more complex task than it is for moteurs de recherche. On platforms such as ChatGPT, search is conversational, which makes results more contextual and personalised, and Par conséquent more difficult to categorise.

Profound: an SEO tool to master pour vos holiday stratégie

SEO tools have recently emerged to enable brands to obtain information about how they are mentioned in conversations. Profound, en particulier, is one of the leaders in this market. This tool generates multiple conversations using variations to run them several times, afin de identify the brands most recommended by different platforms. Through this technology, brands can En savoir plus about their own visibility in the responses provided by platforms and adapt their SEO stratégie accordingly, targeting relevant mots-clés.

Based on a study conducted on toy retailers, the moteurs de recherche Land site web revealed some relevant information provided by Profound. The analysis tool can, en particulier, provide a ranking of visibilité de la marque on a given topic, based on conversations generated on propulsé par l'IA search platforms. The percentage shown Par conséquent corresponds to the rate of AI responses that mention the brand in question:

Please note that the visibility of each brand may differ depending on the platform, as the models used are not the same:

It is also possible to determine the position of each competing brand for each of the different themes provided to Profound:

Digital relationships: the secret to an SEO stratégie that works on SearchGPT

But then, how did the brands mentioned above become benchmarks in their field? It’s all about SEO, so it’s worth taking a look at the sources used by AI to provide its answers.

Par exemple, on the subject of toys, it was mainly the content on the TinyBeans site web, aimed at parents, that had the greatest influence on the results. Autrement dit, this site is considered the leading authority on toy-related content, Selon various basées sur l'IA search models.

Among the top four results, there are no fewer than three content publishers, namely TinyBeans, Forbes and Parents. Conversely, content provided by the biggest brands in this market, such as Lego and Melissa & Doug, only appear at the bottom of the top 10.

While the analysis carried out on the Profound tool is not sufficient to draw a definitive conclusion about how referencing works, a clear trend seems to be emerging. Unlike moteurs de recherche such as Google, which favour the quality of content provided by the brand afin de highlight it on results pages, it is third-party content that seems to take precedence on SearchGPT platforms.

Par conséquent, for a brand to perform well in the results delivered by these platforms, it must first and foremost be promoted within recognised sources. en plus to the SEO recommendations already mentioned, brands would be well advised to mettre en œuvre digital public relations stratégies and work in collaboration with quality content publishers afin de gain a competitive edge over their rivals.

If you would like to increase votre visibilité on l'IA générative engines, please do not hesitate to contact our SEO agency pour optimiser your site web.