Category: Keyword Research
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Universal Query Classifier
Generalist, Open‑Set Classification for Any Label Taxonomy We’ve developed a search query classifier that takes any list of labels you hand it at inference time and tells you which ones match each search query. No retraining, ever. Just swap in new labels as they appear. Old workflow Pain New workflow Build + label data + retrain…
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LLM-Based Search Volume Prediction
We put Google’s Gemini to the test by comparing its keyword volume predictions to actual search data from Google Search Console (GSC). Here’s what we learned and how we did it. How We Collected and Compared the Data What Did We Find? 1. Direct Correlation Is Weak-to-Moderate 2. Bucket Accuracy: More Forgiving, Still Limited 3.…
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Beyond Rank Tracking: Analyzing Brand Perceptions Through Language Model Association Networks
This post is based on the codebase and specifications for AI Rank, an AI visibility and rank tracking framework developed by DEJAN AI team: https://airank.dejan.ai/ Abstract: Traditional SEO has long relied on rank tracking as a primary metric of online visibility. However, modern search engines, increasingly driven by large language models (LLMs), are evolving beyond…
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ILO
The ILO App: A Step-by-Step Tool for Managing SEO Data and Improving Link Structures Managing SEO efficiently can be a complicated process, especially for websites with a large number of pages. The ILO app aims to simplify this by offering a structured, step-by-step approach. It brings together tools for handling key aspects of SEO, like…
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Query Intent via Retrieval Augmentation and Model Distillation
The paper, titled “QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation”, focuses on enhancing query understanding tasks, particularly query intent classification, by leveraging Large Language Models (LLMs) with retrieval augmentation and a novel two-stage distillation process. Retrieval Augmentation: The paper proposes the use of retrieval augmentation to provide LLMs with…
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Search Query Quality Classifier
We build on the work by Manaal Faruqui and Dipanjan Das from Google AI Language team to train a search query classifier of well-formed search queries. Our model offers a 10% improvement over Google’s classifier by utilising ALBERT architecture instead of LSTM. With accuracy of 80%, the model is production ready and has already been…