All You Can EEAT: Navigating SEO in a Generative AI World

Transcript

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   Salutations!
   I’m Mike King
   (@iPullRank)
   

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   I don’t think you came here for me to tell you
   to make great content and use real authors.
   

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   You Can Google EEAT Best
   Practices (or just look at
   TheZebra.com)
   Write high quality content on
   subjects you actually know things
   about.
   Have an author bio and page with
   links out to other places you write
   about similar subjects and links to
   your social media.
   Make sure those sources
   highlight your expertise. Who you
   write for, where you studied,
   books you’ve written, etc.
   Get links from and give links to
   authoritative sources and people
   on similar subjects.
   Make sure the user experience on
   the sites you write on is great too.
   

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   I think you came here because you want to
   know the nature of the threats ahead.
   

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   After All, Organic Search is Likely Most of Your Traffic
   

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    A Threat to Google is a Threat to You
    

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    TikTok Supplanted Google
    

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    ChatGPT was a code red for Google
    

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    ChatGPT is now the Star Trek
    computer Google wants to be.
    

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    Users believe Google Search quality
    is on a steep decline
    

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    The media is amplifying this idea
    

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    Google missed earnings earlier this
    year because ad sales were down
    

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    The DOJ is coming for Google and
    secrets are coming out
    

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    This is from a post from the Google
    Cloud team discussing how their
    Search product works.
    Although Google has been
    telling on itself for years
    

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    These threats will impact you as you look to
    do content marketing and SEO
    

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    The TikTok Threat will
    Mean More Visual Content
    Ranking
    To compete with the visual content
    channels, Google is surfacing more
    visual content in the SERPs and
    adding more features that allow users
    to get exactly where they want to go.
    This will threaten standard Organic
    positions for web content.
    

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    Short Form Video is About to Get More Competitive
    The video and image real estate in Google is going to become even more competitive since marketers recognize short
    form video as high ROI and the primary way to reach Gen Z.
    

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    Publish Your Short Form
    Video on your site
    A primary mistake that content
    marketers make is only publishing
    their short form videos on a channel
    like TikTok, Instagram, or YouTube.
    You should also publish them on your
    site using tools like Wistia and
    marking them up so they can appear
    in the SERPs.
    

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    Ad Sales Being Down Means more Ads
    What’s up with all this
    whitespace?
    What’s up with this
    featured snippet?
    

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    The real estate will get smaller, so your content
    must be that much more effective when it shows
    up in the SERPs.
    

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    The Last Time People Said
    Search Quality Was Bad we
    Got Panda and Penguin
    Panda fundamentally changed
    Organic Search. You could no longer
    create “SEO content” and rank. The
    SEO community then embraced
    content marketing knowing that it’s
    the only way forward with creating
    content that yields utility.
    Penguin did the same for links.
    Google’s Helpful Content update
    could be the new sheriff in town.
    

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    The Threat of Generative AI
    

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    47% Are Increasing their Blog Content
    

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    Marketers Have the Highest Adoption of Generative AI
    

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    There’s a Growing List of Generative AI Tools
    

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    If you’re using
    ChatGPT, you
    need AIPRM
    for prompt
    management.
    

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    If your prompt is just one sentence, don’t be
    surprised when you get garbage back.
    

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    Every tool on earth is integrating ChatGPT
    

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    Now we have AutoGPT that can do a series of
    tasks without prompts.
    

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    Doug Kessler Warned us
    Back in 2013
    Marketers are about to ramp up the
    content marketing deluge.
    https://blog.hubspot.com/blog/tabid/
    6307/bid/34080/Why-Marketers-Nee
    d-to-Rise-Above-the-Deluge-of-Crappy-
    Content.aspx
    

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    Google’s Loosened their Stance on Generated Content
    

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    I don’t believe Google can reliably
    detect LLM content.
    

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    OpenAI Can’t Even Reliably
    Detect It
    Sure, there are a variety of tools out
    there that “detect” generative AI
    content. However, they are all
    unreliable in that they can yield both
    false negatives and false positives.
    Even the people who built the best
    generative AI tools can only correctly
    detect it at 26% accuracy.
    

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    But Google can use it as a signal
    among other signals.
    

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    There are Reports that Some Sites Using Generative AI Have Been Crushed
    These are sites that don’t edit the content prior to publishing, so they deserve it.
    

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    The Helpful Content Update is Finally Showing its Teeth
    Google has been working on getting the Helpful Content classifier right. The early iterations had limited impact, but now sites
    are getting smacked left and right. We’re also seeing the threshold for crawling and indexing pages has been raised.
    

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    A Lot of “Niche” Sites are Getting Smacked
    

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    I Believe This is a Function of Information Gain
    Conceptually, as it relates to search engines, Information Gain is the measure of how much unique information a given document
    adds to the ranking set of documents. In other words, what are you talking about that your competitors are not?
    

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    Google’s Information Gain
    Patent
    Google’s patent indicates that they are
    specifically scoring for documents that
    feature net new information over other
    documents on the same topic.
    

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    So Many People Are Just Creating Copycat Content
    WHAT GENERATIVE AI MEANS FOR GOOGLE SEARCH
    

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    If you want to survive what’s coming, you’ll need
    to deliver stronger content than everyone else.
    

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    The only content you
    should be making
    

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    Confession.
    

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    I think EEAT is silly.
    

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    I call it E-TEA instead.
    I call it E-TEA instead.
    

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    How When Authorship Markup Tops out at 3%?
    

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    How When Authorship Markup Tops out at 3%?
    And this markup does not always
    specify the author!
    

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    How I Actually Started to Believe in
    E-TEA (or How our Understanding of
    Search is out of date)
    

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    At a Base Level, This is
    What all Search Engines
    Do
    Fundamentally, this is the basis of
    how search engines function. Google
    has developed many layers on top of
    this, but this is the core of what they
    all do.
    

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    Google’s High-Level Pipeline Abstraction
    

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    We know this, but there is a single set of
    innovations that sped Google past the SEO
    community.
    

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    Lexical Search vs Semantic Search are the Two Primary Search Models
    What we as the SEO community do not have a strong enough handle on is that most of what Google’s doing is on the
    semantic side and that has all improved dramatically over the last 10 years based on machine learning.
    

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    Vector Space Model
    Documents and queries are plotted in
    multidimensional vector space. The
    closer a document vector is to a query
    vector, the more relevant it is.
    

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    Words are Converted to Multi-dimensional Coordinates in Vector Space
    

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    This Allows for
    Mathematical Operations
    Comparisons of content and
    keywords become linear algebraic
    operations.
    

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    Relevance is a Function of Cosine Similarity
    When we talk about relevance, it’s the question of similar is determined by how similar the vectors are between documents
    and queries. This is a quantitative measure, not the qualitative idea of how we typically think of relevance.
    

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    TF-IDF Vectors
    The vectors in the vector space model were built from TF-IDF. These were simplistic based on the Bag-of-Words model and
    they did not do much to encapsulate meaning.
    

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    Word2Vec Gave Us
    Embeddings
    Word2Vec was an innovation led by
    Tomas Milosevic and Jeff Dean that
    yielded an improvement in natural
    language understanding by using
    neural networks to compute word
    vectors.
    These were better at capturing
    meaning.
    Many follow-on innovations like
    Sentence2Vec and Doc2Vec would
    follow.
    

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    We Went from Sparse Embeddings to Dense Embeddings
    

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    Word2Vec Captured Relationship, but Not Context – BERT Captures
    Context
    

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    BERT Yields Embeddings with Higher Dimensionality and Information
    Capture
    

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    69 Source:
    https://cloud.google.com/blog/topics/developers-practitioners/find-anything-blazingly-fast-googl
    es-vector-search-technology
    

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    Dense Retrieval
    You remember “passage ranking?”
    This is built on the concept of dense
    retrieval wherein there are more
    embeddings representing more of the
    query and the document to uncover
    deeper meaning.
    

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    Dense Retrieval is Scoring down to the Sentence Level
    

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    Introducing Google’s
    Version of Dense Retrieval
    Google introduces the idea of “aspect
    embeddings” which is series of
    embeddings that represent the full
    elements of both the query and the
    document and give stronger access
    to deeper information.
    

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    Dense Representations for
    Entities
    Google has improved its entity
    resolution using embeddings giving
    them stronger access to information
    in documents.
    

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    Embeddings = Google really understands
    content relevance now.
    

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    Website Representation
    Vectors
    Just as there are representations of
    pages as embeddings, there are
    vectors representing websites and
    Google has recently made
    improvements in understanding when
    content is not relevant to a given site.
    

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    Author Vectors
    Similarly, Google has Author Vectors
    wherein they are able to identify an
    author and the subject matter that
    they discuss. This allows them to
    fingerprint an author and their
    expertise.
    

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    So, really E-TEA is a function of information
    associated with vector representations of
    websites and authors.
    

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    As a content marketer, you need to treat your
    byline like the asset that it is.
    

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    Also, relevance isn’t qualitative to Google.
    

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    Embeddings keep getting better at capturing
    meaning while SEO tools still operate on the
    Lexical Search model
    

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    I Feel Like My Page is More Relevant to [Enterprise SEO]
    

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    Relevance isn’t Qualitative to Google.
    

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    See! My page is more
    relevant, but it’s not
    ranking as well.
    

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    https://ipullrank.com/tools/orbitwise
    

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    The threat of Google’s Search
    Generative Experience (SGE)
    

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    At I/O Google Announced a
    Dramatic Change to
    Search
    The experimental “Search Generative
    Experience” brings generative AI to
    the SERPs and significantly changes
    Google’s UX.
    

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    Queries are Longer and the
    Featured Snippet is Bigger
    1. The query is more natural language
    and no longer Orwellian Newspeak. It
    can be much longer than the 32
    words that is has been historically in
    order
    2. The Featured Snippet has become
    the “AI snapshot” which takes 3
    results and builds a summary.
    3. Users can also ask follow up
    questions in conversational mode.
    3
    2
    1
    

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    Sundar is All In.
    In Sundar’s recent press run he keeps
    saying how Google will be doubling
    down on SGE. So it’s going to be a thing
    moving forward.
    

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    The Search Demand Curve
    will Shift
    With the change in the level of natural
    language query that Google can
    support, we’re going to see a lot less
    head terms and a lot more long tail
    term.
    Going
    down
    Going
    up
    

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    The CTR Model Will
    Change
    With the search results being pushed
    down by the AI snapshot experience,
    what is considered #1 will change.
    We should also expect that any
    organic result will be clicked less and
    the standard organic will drop
    dramatically.
    However, this will likely yield query
    displacement.
    

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    Rank Tracking Will Be
    More Complex
    As an industry, we’ll need to decide
    what is considered the #1 result.
    Based on this screenshot positions
    1-3 are now the citations for the AI
    snapshot and #4 is below it.
    However, the AI snapshot loads on
    the client side, so rank tracking tools
    will need to change their approach.
    

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    Context Windows Will
    Yield More Personalized
    Results
    SGE maintains the context window of
    the previous search in the journey as
    the user goes through predefined
    follow questions.
    This will need to drive the
    composition of pages to ensure they
    remain in the consideration set for
    subsequent results.
    

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    Ask to Trigger.
    

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    Auto-Trigger.
    

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    We’ve seen this take up to
    30 seconds to generate.
    Although, it’s a lot faster
    now.
    

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     HELLO EMPTY
     REAL ESTATE!
     (aka more ad clicks)
     

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     It’s an “experiment” so we don’t know much,
     but here’s what we can infer.
     

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     Most autoloading AI Snapshots take 3-30
     seconds to load.
     

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     We Know that Featured Snippets Take 35.1% of Clicks
     

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     Using All that Information We’re Modeling the Threat of SGE
     

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     Get your threat report:
     https://ipullrank.com/sge-report
     

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     What is Retrieval Augmented
     Generation (RAG)?
     

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     This is Called “Retrieval Augmented Generation”
     Neeva (RIP), Bing, and now Google’s Search Generative Experience all use pull documents based on search queries and
     feed them to a language model to generate a response.
     

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     Google’s Version of this is called Retrieval-Augmented Language Model
     Pre-Training (REALM) from 2021
     

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     SGE is built from REALM + PaLM 2 and MUM
     MUM is the Multitask Unified Model that Google announced in 2021 as way to do retrieval augmented generation. PaLM 2
     is their latest state of the art large language model.
     

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     If You Want More
     Technical Detail Check Out
     This Paper
     https://arxiv.org/pdf/2002.08909.pdf
     

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     Search Engines Are Now
     OK with Not Being Right
     They evaluate Bing Chat, NeevaAI,
     http://perplexity.ai & YouChat—only 52%
     of statements are supported by citations
     and 75% of citations actually support
     their statements.
     https://arxiv.org/abs/2304.09848
     

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     Sounds cool, but how does it work?
     

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     It’s so easy that I built a proof of concept
     

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     AvesAPI + Llama Index + ChatGPT = Raggle
     Rankings
     data
     Vector index
     & operations
     Clearly you
     know what
     this does.
     

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     It’s pretty simple
     # Make an index from your documents
     index = VectorStoreIndex.from_documents(documents)
     # Setup your index for citations
     query_engine = CitationQueryEngine.from_args(
     index,
     # indicate how many document chunks it should return
     similarity_top_k=5,
     # here we can control how granular citation sources are, the default is 512
     citation_chunk_size=155,
     )
     response = query_engine.query("Answer the following query in 150 words: " + query)
     

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     Limitations of my POC
     It doesn’t do follow up questions It’s not responsive It only does the informational snippet
     

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     You can play with it at raggle.net
     

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     Optimizing for SGE?
     

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     Dense Retrieval
     You remember “passage ranking?”
     This is built on the concept of dense
     retrieval wherein there are more
     embeddings representing more of the
     query and the document to uncover
     deeper meaning.
     

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     Dense Retrieval is Scoring down to the Sentence Level
     

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     It’s all about the chunks. So use Llama Index to
     determine the your chunks and improve the
     similarity to the query.
     

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     I’ve Added A Chunk Explorer so You Can See Which Text was Used
     

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     The Content Opportunity of RAG
     

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131. There’s a Lot of
     Synergy Between
     KGs and LLMs
     There are three models gaining
     popularity:
     1. KG-enhanced LLMs -
     Language Model uses KG
     during pre-training and
     inference
     2. LLM-augmented KGs - LLMs
     do reasoning and completion
     on KG data
     3. Synergized LLMs + KGs -
     Multilayer system using both
     at the same time
     https://arxiv.org/pdf/2306.08302.pdf
     Source: Unifying Large Language Models and Knowledge Graphs: A Roadmap
     

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132. Organizations
     are doing RAG
     with Knowledge
     Graphs
     ● Anyone can feed their
     data into an LLM as a
     fine-tuning measure to
     improve the output.
     ● People are currently
     using their knowledge
     graphs to support this.
     

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     The code is not much different
     sitemap_url = "[SITEMAP URL]"
     sitemap = adv.sitemap_to_df(sitemap_url)
     urls_to_crawl = sitemap['loc'].tolist()
     ...
     # Make an index from your documents
     index = VectorStoreIndex.from_documents(documents)
     # Setup your index for citations
     query_engine = CitationQueryEngine.from_args(
     index,
     # indicate how many document chunks it should return
     similarity_top_k=5,
     # here we can control how granular citation sources are, the default is 512
     citation_chunk_size=155,
     )
     response = query_engine.query("YOUR PROMPT HERE")
     

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134. Fact
     Verification
     ● Although Google has
     historically said they do
     not verification of facts.
     ● LLM + KG integrations
     make this a possibility
     and Google needs to
     combat the wealth of
     content being produced
     with LLMs. So, it’s likely
     they will use this
     functionality.
     Source: Fact Checking in Knowledge
     Graphs by Logical Consistency
     Source: FactKG: Fact Verification via Reasoning on Knowledge Graphs
     

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     Brands are Using Generative AI as a Force Multiplier
     ● 52% of business leaders are currently using
     AI content generation tools to assist their
     content marketing efforts.
     ● 64.7% of business leaders plan to use AI
     content generation tools to assist their
     content marketing efforts in 2023.
     Major brands are using tools like ChatGPT and Midjourney to scale their content marketing efforts. The brands that don’t
     leverage these tools are quickly falling behind.
     Source: Siege Media + Clearscope
     

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     Sadly, Everyone is Using it and No One Has a Strategy
     

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     But… Brands Still Need Content
     Strategy to Capitalize On It
     Individuals are using tools like ChatGPT
     in isolation, but for an organization to
     capitalize on it there needs to be a
     generative AI content strategy that
     encourages governance and
     consistency of the content created.
     

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     The Three Laws of Generative AI content
     1. Generative AI is not the end-all-be-all solution.
     It is not the replacement for a content strategy or
     your content team.
     2. Generative AI for content creation should be a force
     multiplier to be utilized to improve workflow and
     augment strategy.
     3. You should consider generative AI content for
     awareness efforts, but continue to leverage subject
     matter experts for lower funnel content.
     GENERATIVE AI OPPORTUNITIES & THREATS
     

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     How We’re Helping Brands Capitalize on Generative AI
     Leveraging our extensive enterprise
     Content Strategy experience, we
     take an 8-step approach to make
     generative AI tools learn to speak in
     your brand voice and we build out
     solutions to bake the functionality
     into your toolkit.
     We take a deep
     dive into how your
     Content Strategy
     currently operates
     to replicate and
     expand on it
     through AI.
     We look for places
     in your existing
     processes and
     tools to integrate
     AI functionality.
     We build out the
     content models,
     workflows,
     governance
     models, and toolkit
     for generative AI.
     We develop a
     library of prompts
     to be used across
     your organization
     for various content
     use cases.
     We run the
     prompts through a
     series of QA tests
     to ensure that
     content is always
     generated as
     expected.
     We improve
     prompts that do
     not pass our QA
     tests. We deliver the
     prompts and
     training on how to
     use the new
     content systems.
     We update and
     optimize prompts
     as generative AI
     tools update and
     emerge.
     Strategic Planning
     We tailor our
     approach to your
     goals and existing
     content strategy.
     Generative AI
     Delivery
     We deliver vetted
     prompts and train
     your team on
     generative AI
     systems.
     Review Client
     Goals and
     Content
     Strategy
     Identify AI
     integration
     points
     Prepare
     Generative AI
     Content Plan
     Output QA
     Build Prompt
     Library
     Optimize
     Outputs
     Knowledge
     Transfer
     Maintenance
     OUR GENERATIVE AI PROCESS
     

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     Don’t forget that ChatGPT is very much an
     unfinished product.
     

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141. Custom Index Functionality Coming
     Very Soon
     

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     2
     Roll the Credits
     

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     Mike, that was a lot. What should I be doing?
     Write with Information Gain in mind
     Keep an eye on threats in the SERPs
     Use structured data wherever possible
     Use tools to understand how relevant Google thinks your content is
     Build an actual content strategy around generative AI
     Build a prompt library
     Build custom indexes for stronger generative AI content creation
     Treat your byline as the asset that it is
     By ready for search behavior to change
     Optimize the chunks
     

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     We’ve Been Using GPT Tech Since 2020
     GENERATIVE AI OPPORTUNITIES & THREATS
     

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     Get your threat report:
     https://ipullrank.com/sge-report
     

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147. Custom Index Functionality Coming
     Very Soon
     

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149. Mike King
     Founder / CEO
     @iPullRank
     Thank You | Q&A
     mike@ipullrank.com
     Award Winning, #GirlDad
     Featured by
     Get Your SGE Threat Report:
     https://ipullrank.com/sge-report
     Play with Raggle:
     https://www.raggle.net
     

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