When it comes to optimising content for AI Overviews, most advice tends to focus on familiar SEO practices: aligning with user intent, writing structured and comprehensive content, targeting long-tail keywords, keeping information up to date, and using schema markup. Yes, these tactics do help ensure content is clear, relevant, and technically sound, all of which improve the chances of being featured in AI overviews.
But one angle that’s often overlooked is how AI systems measure content relevance – and that’s where cosine similarity comes in. According to Wikipedia, cosine similarity is a measure of similarity between two non-zero vectors defined in an inner product space. In simpler terms, it’s a mathematical way to assess how similar two pieces of text are, based on the direction of their word vectors rather than their raw content.
In this blog post, I’ll break down what cosine similarity is, how it applies to SEO in the era of Generative AI, and how to use a free tool I built to analyse and optimise your content accordingly.
With Google’s Search Generative Experience (SGE) and AI Overviews becoming a bigger part of the SERPs, SEO has entered a new frontier. It’s no longer just about ranking on page one – now, you also want your content to align with what Google’s AI Overviews are saying, so you have a better chance of being cited as a trusted source.
Is my content semantically aligned with the Generative AI-generated summaries Google is showing users?”
What Is Cosine Similarity?
Cosine similarity is a metric that evaluates how similar two pieces of text are, regardless of their length. It does this by representing each text as a vector and then calculating the cosine of the angle between them.
- If the angle is small (close to 0°), the cosine similarity approaches 1.0, meaning the texts are very similar.
- If the angle is large (close to 90°), the cosine similarity approaches 0.0, meaning they have little in common.
In practical terms:
- 1.0 = texts are nearly identical
- 0.7+ = highly similar
- 0.4 – 0.7 = moderately similar
- < 0.4 = weak semantic relationship
This method measures the similarity between two pieces of text by calculating the cosine of the angle between their vector representations. A higher cosine similarity score indicates a stronger semantic relationship.
Why It Matters for SEO and AI Overviews?
Google’s AI Overviews are designed to provide users with concise, helpful answers at the top of the search results. These summaries pull from content Google considers authoritative and aligned with the query’s intent.
So if your content mirrors the semantic meaning, entity coverage, and tone of an AI Overview – aligning with the user’s intent and focusing on relevant terms rather than keyword stuffing, your chances of being referenced or rewarded by Google significantly improve.
The Tool: Compare Your Content Against AI Overviews
To help with this, I built a Google Colab-based tool that allows you to directly compare your content with an AI Overview text and see where you align and where you don’t.
You simply:
- Paste your website content.
- Paste the AI Overview text (copied from the SERP).
- Instantly get a cosine similarity score.
- View found vs missing key phrases (n-grams)
- See the AI Overview highlighted in color-coded phrases to audit visually
What does the Tool do?
Once you input both texts, the tool does three things:
1. Cosine Similarity Score
It shows how semantically close your content is to the AI Overview. A score closer to 1.0 means your content shares similar phrasing and likely covers the same topics.
This isn’t just about matching keywords — it’s about aligning with intent and usefulness, just like Google’s AI does.
2. N-gram Coverage Breakdown
It splits the AI Overview into short phrases – unigrams, bigrams, and trigrams and tells you:
- Which n-grams are already present in your content
- Which n-grams are missing from your content
It prioritises highlighting terms from the first sentence of the AI Overview, since that often contains the core search intent.
The rest of the n-grams are available in a collapsible view, so you don’t get overwhelmed, but still have access to a full gap analysis.
Instead of keyword stuffing, the goal here is to align with the semantic meaning and entity richness that AI Overviews prioritise.
3. Visual Highlighting of the AI Overview
The full AI Overview is then displayed with:
- Green highlights for phrases found in your content
- Red highlights for missing phrases
Try the Tool Now
The tool runs directly in Google Colab (no installation required).
Open the Content vs AI Overview Comparison Tool
Just paste your content, paste the AI Overview, and click “Calculate”. You’ll get:
- A similarity score
- Phrase-by-phrase match analysis
- A highlighted AI Overview showing gaps
How to Use It for Content Optimisation?
Here’s how you can use this tool to create highly relevant content;
Align with Intent
By checking which parts of the AI Overview your content already covers (or doesn’t), you can rewrite or enhance your pages to match the searcher’s intent more closely.
Expand Entity Coverage
Google’s AI looks for topical depth, not just keywords. If the Overview mentions “periodisation in marathon training” and your blog only says “follow a running plan,” that’s a semantic gap you can close.
After refreshing or updating content, you can rerun the tool and see if your cosine similarity score improves, giving you measurable feedback.
Final Thoughts
As AI Overviews and generative search become the new normal, SEOs need to adapt. It’s no longer enough to rank your content also needs to reflect what users expect to see summarised by AI.
Cosine similarity offers a powerful, math-backed way to evaluate how closely your content aligns with AI-generated responses.
Use this tool to audit, improve, and future-proof your content.
Let’s Talk
Got feedback or want to collaborate on more tools like this? I’d love to hear how you’re using AI in your workflow.
Find me on [LinkedIn] or drop a comment below.