From 37de08db16c635930af86ac2e390908ac11920e9 Mon Sep 17 00:00:00 2001
From: kanungle <neil.kanungo@gmail.com>
Date: Mon, 22 Jun 2026 22:08:34 -0700
Subject: [PATCH] initial category reorganization

---
 .../content/articles/agentic-builders-guide.md        |  2 +-
 qdrant-landing/content/articles/agentic-rag.md        |  2 +-
 .../articles/batch-vector-search-with-qdrant.md       |  2 +-
 .../content/articles/binary-quantization-openai.md    |  2 +-
 .../content/articles/binary-quantization.md           |  2 +-
 qdrant-landing/content/articles/bm42.md               |  2 +-
 qdrant-landing/content/articles/cars-recognition.md   |  3 ++-
 .../content/articles/core-concepts/_index.md          |  9 +++++++++
 .../articles/cross-encoder-integration-gsoc.md        |  2 +-
 .../content/articles/data-exploration/_index.md       |  2 +-
 qdrant-landing/content/articles/data-privacy.md       |  2 +-
 qdrant-landing/content/articles/dedicated-service.md  |  2 +-
 .../content/articles/dedicated-vector-search.md       |  2 +-
 .../content/articles/demos-and-tutorials/_index.md    |  9 +++++++++
 .../content/articles/detecting-coffee-anomalies.md    |  3 ++-
 .../content/articles/dimension-reduction-qsoc.md      |  2 +-
 qdrant-landing/content/articles/ecosystem/_index.md   |  9 ---------
 qdrant-landing/content/articles/embedding-recycler.md |  3 ++-
 .../content/articles/embedding-research/_index.md     |  9 +++++++++
 .../content/articles/faq-question-answering.md        |  3 ++-
 qdrant-landing/content/articles/fastembed.md          |  2 +-
 .../content/articles/food-discovery-demo.md           |  2 +-
 .../content/articles/geo-polygon-filter-gsoc.md       |  2 +-
 .../articles/how-to-choose-an-embedding-model.md      |  2 +-
 qdrant-landing/content/articles/hybrid-search.md      |  2 +-
 .../content/articles/indexing-optimization.md         |  2 +-
 .../content/articles/langchain-integration.md         |  2 +-
 .../content/articles/late-interaction-models.md       |  2 +-
 .../content/articles/machine-learning/_index.md       |  9 ---------
 .../content/articles/mastering-search/_index.md       |  9 +++++++++
 qdrant-landing/content/articles/memory-consumption.md |  2 +-
 .../content/articles/metric-learning-tips.md          |  2 +-
 qdrant-landing/content/articles/miniCOIL.md           |  2 +-
 .../articles/modern-sparse-neural-retrieval.md        |  2 +-
 qdrant-landing/content/articles/multitenancy.md       |  2 +-
 qdrant-landing/content/articles/muvera-embeddings.md  |  2 +-
 .../content/articles/neural-search-tutorial.md        |  2 +-
 .../content/articles/practicle-examples/_index.md     |  9 ---------
 .../content/articles/product-quantization.md          |  2 +-
 .../content/articles/production-ops/_index.md         |  9 +++++++++
 .../content/articles/qa-with-cohere-and-qdrant.md     |  3 ++-
 .../content/articles/qdrant-internals/_index.md       |  2 +-
 .../content/articles/rag-and-agents/_index.md         |  9 +++++++++
 .../content/articles/rag-and-genai/_index.md          |  9 ---------
 qdrant-landing/content/articles/rag-is-dead.md        |  2 +-
 ...rapid-rag-optimization-with-qdrant-and-quotient.md |  2 +-
 qdrant-landing/content/articles/relevance-feedback.md |  2 +-
 .../content/articles/scalar-quantization.md           |  2 +-
 qdrant-landing/content/articles/search-as-you-type.md |  2 +-
 .../content/articles/search-feedback-loop.md          |  2 +-
 .../content/articles/search-quality/_index.md         |  9 +++++++++
 .../articles/semantic-cache-ai-data-retrieval.md      |  2 +-
 qdrant-landing/content/articles/serverless.md         |  2 +-
 .../articles/sparse-embeddings-ecommerce-part-1.md    |  2 +-
 .../articles/sparse-embeddings-ecommerce-part-2.md    |  2 +-
 .../articles/sparse-embeddings-ecommerce-part-3.md    |  2 +-
 .../articles/sparse-embeddings-ecommerce-part-4.md    |  2 +-
 .../articles/sparse-embeddings-ecommerce-part-5.md    |  2 +-
 qdrant-landing/content/articles/sparse-vectors.md     |  2 +-
 .../storing-multiple-vectors-per-object-in-qdrant.md  |  2 +-
 qdrant-landing/content/articles/triplet-loss.md       |  2 +-
 .../content/articles/vector-search-filtering.md       |  2 +-
 .../content/articles/vector-search-manuals/_index.md  |  9 ---------
 .../content/articles/vector-search-production.md      |  2 +-
 .../articles/vector-search-resource-optimization.md   |  2 +-
 qdrant-landing/content/articles/web-ui-gsoc.md        |  2 +-
 .../content/articles/what-are-embeddings.md           |  2 +-
 .../content/articles/what-is-a-vector-database.md     |  2 +-
 .../content/articles/what-is-quantization.md          |  2 +-
 qdrant-landing/content/articles/what-is-rag-in-ai.md  |  2 +-
 ...earch-manuals.md => ref-acticles-core-concepts.md} |  4 ++--
 .../content/learn/ref-acticles-data-exploration.md    |  2 +-
 .../content/learn/ref-acticles-demos-and-tutorials.md | 11 +++++++++++
 .../content/learn/ref-acticles-ecosystem.md           | 10 ----------
 .../content/learn/ref-acticles-embedding-research.md  | 11 +++++++++++
 .../content/learn/ref-acticles-mastering-search.md    | 11 +++++++++++
 .../content/learn/ref-acticles-practicle-examples.md  | 10 ----------
 ...ine-learning.md => ref-acticles-production-ops.md} |  4 ++--
 .../content/learn/ref-acticles-qdrant-internals.md    |  2 +-
 ...ag-and-genai.md => ref-acticles-rag-and-agents.md} |  6 +++---
 .../content/learn/ref-acticles-search-quality.md      | 11 +++++++++++
 qdrant-landing/static/_redirects                      |  7 +++++++
 82 files changed, 186 insertions(+), 132 deletions(-)
 create mode 100644 qdrant-landing/content/articles/core-concepts/_index.md
 create mode 100644 qdrant-landing/content/articles/demos-and-tutorials/_index.md
 delete mode 100644 qdrant-landing/content/articles/ecosystem/_index.md
 create mode 100644 qdrant-landing/content/articles/embedding-research/_index.md
 delete mode 100644 qdrant-landing/content/articles/machine-learning/_index.md
 create mode 100644 qdrant-landing/content/articles/mastering-search/_index.md
 delete mode 100644 qdrant-landing/content/articles/practicle-examples/_index.md
 create mode 100644 qdrant-landing/content/articles/production-ops/_index.md
 create mode 100644 qdrant-landing/content/articles/rag-and-agents/_index.md
 delete mode 100644 qdrant-landing/content/articles/rag-and-genai/_index.md
 create mode 100644 qdrant-landing/content/articles/search-quality/_index.md
 delete mode 100644 qdrant-landing/content/articles/vector-search-manuals/_index.md
 rename qdrant-landing/content/learn/{ref-acticles-vector-search-manuals.md => ref-acticles-core-concepts.md} (81%)
 create mode 100644 qdrant-landing/content/learn/ref-acticles-demos-and-tutorials.md
 delete mode 100644 qdrant-landing/content/learn/ref-acticles-ecosystem.md
 create mode 100644 qdrant-landing/content/learn/ref-acticles-embedding-research.md
 create mode 100644 qdrant-landing/content/learn/ref-acticles-mastering-search.md
 delete mode 100644 qdrant-landing/content/learn/ref-acticles-practicle-examples.md
 rename qdrant-landing/content/learn/{ref-acticles-machine-learning.md => ref-acticles-production-ops.md} (83%)
 rename qdrant-landing/content/learn/{ref-acticles-rag-and-genai.md => ref-acticles-rag-and-agents.md} (78%)
 create mode 100644 qdrant-landing/content/learn/ref-acticles-search-quality.md

diff --git a/qdrant-landing/content/articles/agentic-builders-guide.md b/qdrant-landing/content/articles/agentic-builders-guide.md
index ae498e8c9..99794a356 100644
--- a/qdrant-landing/content/articles/agentic-builders-guide.md
+++ b/qdrant-landing/content/articles/agentic-builders-guide.md
@@ -7,7 +7,7 @@ social_preview_image: /articles_data/agentic-builders-guide/preview/social_previ
 author: Thierry Damiba
 draft: false
 date: 2025-10-26T00:00:00.000Z
-category: rag-and-genai
+category: rag-and-agents
 ---
 ## Overview
 
diff --git a/qdrant-landing/content/articles/agentic-rag.md b/qdrant-landing/content/articles/agentic-rag.md
index 057ecc64d..6a2bdbadf 100644
--- a/qdrant-landing/content/articles/agentic-rag.md
+++ b/qdrant-landing/content/articles/agentic-rag.md
@@ -8,7 +8,7 @@ weight: -150
 author: Kacper Łukawski
 author_link: https://www.kacperlukawski.com
 date: 2024-11-22T00:00:00.000Z
-category: rag-and-genai
+category: rag-and-agents
 ---
 
 Standard [Retrieval Augmented Generation](/articles/what-is-rag-in-ai/) follows a predictable, linear path: receive 
diff --git a/qdrant-landing/content/articles/batch-vector-search-with-qdrant.md b/qdrant-landing/content/articles/batch-vector-search-with-qdrant.md
index 13ca43cd8..36a806e16 100644
--- a/qdrant-landing/content/articles/batch-vector-search-with-qdrant.md
+++ b/qdrant-landing/content/articles/batch-vector-search-with-qdrant.md
@@ -13,7 +13,7 @@ tags:
   - Vector Database
   - Machine Learning
   - Information Retrieval
-category: vector-search-manuals
+category: mastering-search
 ---
 
 # How to Optimize Vector Search Using Batch Search in Qdrant 0.10.0
diff --git a/qdrant-landing/content/articles/binary-quantization-openai.md b/qdrant-landing/content/articles/binary-quantization-openai.md
index 0e0556640..4a343ea68 100644
--- a/qdrant-landing/content/articles/binary-quantization-openai.md
+++ b/qdrant-landing/content/articles/binary-quantization-openai.md
@@ -22,7 +22,7 @@ tags:
 weight: -130
 
 aliases: [ /blog/binary-quantization-openai/ ]
-category: practicle-examples
+category: search-quality
 ---
 
 OpenAI Ada-003 embeddings are a powerful tool for natural language processing (NLP). However, the size of the embeddings are a challenge, especially with real-time search and retrieval. In this article, we explore how you can use Qdrant's Binary Quantization to enhance the performance and efficiency of OpenAI embeddings.
diff --git a/qdrant-landing/content/articles/binary-quantization.md b/qdrant-landing/content/articles/binary-quantization.md
index ac64413c6..d61da564a 100644
--- a/qdrant-landing/content/articles/binary-quantization.md
+++ b/qdrant-landing/content/articles/binary-quantization.md
@@ -14,7 +14,7 @@ keywords:
   - vector search
   - binary quantization
   - memory optimization
-category: qdrant-internals
+category: production-ops
 ---
 
 # Optimizing High-Dimensional Vectors with Binary Quantization
diff --git a/qdrant-landing/content/articles/bm42.md b/qdrant-landing/content/articles/bm42.md
index e60107f28..cf54b8d83 100644
--- a/qdrant-landing/content/articles/bm42.md
+++ b/qdrant-landing/content/articles/bm42.md
@@ -12,7 +12,7 @@ keywords:
   - hybrid search
   - sparse embeddings
   - bm25
-category: machine-learning
+category: embedding-research
 ---
 
 <aside role="status">
diff --git a/qdrant-landing/content/articles/cars-recognition.md b/qdrant-landing/content/articles/cars-recognition.md
index 6b5d6d092..8c3605df8 100644
--- a/qdrant-landing/content/articles/cars-recognition.md
+++ b/qdrant-landing/content/articles/cars-recognition.md
@@ -11,7 +11,8 @@ author_link: https://medium.com/@yusufsarigoz
 date: 2022-06-28T13:00:00+03:00
 draft: false
 # aliases: [ /articles/cars-recognition/ ]
-category: machine-learning
+category: embedding-research
+hideFromList: true
 ---
 
 Supervised classification is one of the most widely used training objectives in machine learning,
diff --git a/qdrant-landing/content/articles/core-concepts/_index.md b/qdrant-landing/content/articles/core-concepts/_index.md
new file mode 100644
index 000000000..90d2bdcfb
--- /dev/null
+++ b/qdrant-landing/content/articles/core-concepts/_index.md
@@ -0,0 +1,9 @@
+---
+title: Core Concepts
+short_description: "Foundational concepts for vector search: embeddings, vector databases, quantization, sparse vectors, and RAG."
+description: Start here to understand the building blocks of vector search. Learn what vector databases, embeddings, quantization, and retrieval-augmented generation are and how they fit together.
+category: core-concepts
+url: /articles/core-concepts/
+isCategoryPage: true
+weight: 10
+---
diff --git a/qdrant-landing/content/articles/cross-encoder-integration-gsoc.md b/qdrant-landing/content/articles/cross-encoder-integration-gsoc.md
index 50599dcad..b4645aba6 100644
--- a/qdrant-landing/content/articles/cross-encoder-integration-gsoc.md
+++ b/qdrant-landing/content/articles/cross-encoder-integration-gsoc.md
@@ -15,7 +15,7 @@ keywords:
   - reranking
   - fastembed
   - qsoc'24
-category: machine-learning
+hideFromList: true
 ---
 
 ## Introduction
diff --git a/qdrant-landing/content/articles/data-exploration/_index.md b/qdrant-landing/content/articles/data-exploration/_index.md
index 6f76d3f12..fa38c3e77 100644
--- a/qdrant-landing/content/articles/data-exploration/_index.md
+++ b/qdrant-landing/content/articles/data-exploration/_index.md
@@ -5,5 +5,5 @@ description: Learn how you can leverage vector similarity beyond just search. Re
 category: data-exploration
 url: /articles/data-exploration/
 isCategoryPage: true
-weight: 30
+weight: 80
 ---
diff --git a/qdrant-landing/content/articles/data-privacy.md b/qdrant-landing/content/articles/data-privacy.md
index 640feb812..c0e595737 100644
--- a/qdrant-landing/content/articles/data-privacy.md
+++ b/qdrant-landing/content/articles/data-privacy.md
@@ -15,7 +15,7 @@ keywords: # Keywords for SEO
   - Secure AI Data Management
   - Qdrant Data Security
   - Enterprise Data Compliance
-category: vector-search-manuals
+category: production-ops
 ---
 
 Data stored in vector databases is often proprietary to the enterprise and may include sensitive information like customer records, legal contracts, electronic health records (EHR), financial data, and intellectual property. Moreover, strong security measures become critical to safeguarding this data. If the data stored in a vector database is not secured, it may open a vulnerability known as "[embedding inversion attack](https://arxiv.org/abs/2004.00053)," where malicious actors could potentially [reconstruct the original data from the embeddings](https://arxiv.org/pdf/2305.03010) themselves. 
diff --git a/qdrant-landing/content/articles/dedicated-service.md b/qdrant-landing/content/articles/dedicated-service.md
index c045617d6..6b8f22e2f 100644
--- a/qdrant-landing/content/articles/dedicated-service.md
+++ b/qdrant-landing/content/articles/dedicated-service.md
@@ -15,7 +15,7 @@ keywords:
     - vector search
     - best practices
     - anti-patterns
-category: qdrant-internals
+category: core-concepts
 ---
 
 
diff --git a/qdrant-landing/content/articles/dedicated-vector-search.md b/qdrant-landing/content/articles/dedicated-vector-search.md
index 0783e2c49..775237590 100644
--- a/qdrant-landing/content/articles/dedicated-vector-search.md
+++ b/qdrant-landing/content/articles/dedicated-vector-search.md
@@ -12,7 +12,7 @@ keywords:
     - system architecture
     - vector search
     - vector database 
-category: qdrant-internals
+category: core-concepts
 ---
 
 Any problem with even a bit of complexity requires a specialized solution. You can use a Swiss Army knife to open a bottle or poke a hole in a cardboard box, but you will need an axe to chop wood — the same goes for software.
diff --git a/qdrant-landing/content/articles/demos-and-tutorials/_index.md b/qdrant-landing/content/articles/demos-and-tutorials/_index.md
new file mode 100644
index 000000000..a0ab23b45
--- /dev/null
+++ b/qdrant-landing/content/articles/demos-and-tutorials/_index.md
@@ -0,0 +1,9 @@
+---
+title: Demos & Tutorials
+short_description: "Step-by-step tutorials and demos with Qdrant: neural search, serverless deployments, and framework integrations."
+description: Learn by building. Follow hands-on tutorials and demos covering neural search, serverless deployments, search-as-you-type, and integrations with popular frameworks.
+category: demos-and-tutorials
+url: /articles/demos-and-tutorials/
+isCategoryPage: true
+weight: 90
+---
diff --git a/qdrant-landing/content/articles/detecting-coffee-anomalies.md b/qdrant-landing/content/articles/detecting-coffee-anomalies.md
index 4058250d2..3cb1ff359 100644
--- a/qdrant-landing/content/articles/detecting-coffee-anomalies.md
+++ b/qdrant-landing/content/articles/detecting-coffee-anomalies.md
@@ -10,8 +10,9 @@ author: Yusuf Sarıgöz
 author_link: https://medium.com/@yusufsarigoz
 date: 2022-05-04T13:00:00+03:00
 draft: false
-category: machine-learning
+category: embedding-research
 # aliases: [ /articles/detecting-coffee-anomalies/ ]
+hideFromList: true
 ---
 
 Anomaly detection is a thirsting yet challenging task that has numerous use cases across various industries.
diff --git a/qdrant-landing/content/articles/dimension-reduction-qsoc.md b/qdrant-landing/content/articles/dimension-reduction-qsoc.md
index 24d497854..affd2e45a 100644
--- a/qdrant-landing/content/articles/dimension-reduction-qsoc.md
+++ b/qdrant-landing/content/articles/dimension-reduction-qsoc.md
@@ -17,7 +17,7 @@ keywords:
     - vector similarity
     - tsne
     - qdrant data visualization
-category: ecosystem
+hideFromList: true
 ---
 
 
diff --git a/qdrant-landing/content/articles/ecosystem/_index.md b/qdrant-landing/content/articles/ecosystem/_index.md
deleted file mode 100644
index fc81be8fb..000000000
--- a/qdrant-landing/content/articles/ecosystem/_index.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-title: Ecosystem
-short_description: "Articles covering Qdrant's ecosystem: integrations, embedding providers, frameworks, and tools that pair with vector search."
-description: Tools, libraries and integrations around Qdrant vector search engine.
-category: ecosystem
-url: /articles/ecosystem/
-isCategoryPage: true
-weight: 
----
diff --git a/qdrant-landing/content/articles/embedding-recycler.md b/qdrant-landing/content/articles/embedding-recycler.md
index 8f95c81d3..1b0397be5 100644
--- a/qdrant-landing/content/articles/embedding-recycler.md
+++ b/qdrant-landing/content/articles/embedding-recycler.md
@@ -11,7 +11,8 @@ author_link: https://medium.com/@yusufsarigoz
 date: 2022-08-23T13:00:00+03:00
 draft: false
 aliases: [ /articles/embedding-recycler/ ]
-category: machine-learning
+category: embedding-research
+hideFromList: true
 ---
 
 A recent [paper](https://arxiv.org/abs/2207.04993)
diff --git a/qdrant-landing/content/articles/embedding-research/_index.md b/qdrant-landing/content/articles/embedding-research/_index.md
new file mode 100644
index 000000000..b3b460761
--- /dev/null
+++ b/qdrant-landing/content/articles/embedding-research/_index.md
@@ -0,0 +1,9 @@
+---
+title: Embedding Research
+short_description: "Research on embeddings and neural retrieval: sparse models, late interaction, metric learning, and new baselines."
+description: Explore the research behind modern embeddings and neural retrieval. Dive into sparse neural models, late interaction, metric learning, and new baselines for hybrid search.
+category: embedding-research
+url: /articles/embedding-research/
+isCategoryPage: true
+weight: 60
+---
diff --git a/qdrant-landing/content/articles/faq-question-answering.md b/qdrant-landing/content/articles/faq-question-answering.md
index 4fafd5832..2e3bf300a 100644
--- a/qdrant-landing/content/articles/faq-question-answering.md
+++ b/qdrant-landing/content/articles/faq-question-answering.md
@@ -9,8 +9,9 @@ weight: 9
 author: George Panchuk
 author_link: https://medium.com/@george.panchuk
 date: 2022-06-28T08:57:07.604Z
-category: practicle-examples
+category: demos-and-tutorials
 # aliases: [ /articles/faq-question-answering/ ]
+hideFromList: true
 ---
 
 # Question-answering system with Similarity Learning and Quaterion
diff --git a/qdrant-landing/content/articles/fastembed.md b/qdrant-landing/content/articles/fastembed.md
index 2496fbb67..a21ce3e35 100644
--- a/qdrant-landing/content/articles/fastembed.md
+++ b/qdrant-landing/content/articles/fastembed.md
@@ -19,7 +19,7 @@ keywords:
     - embeddings
     - ONNX Runtime
     - quantized embedding model
-category: ecosystem
+category: demos-and-tutorials
 ---
 
 Data Science and Machine Learning practitioners often find themselves navigating through a labyrinth of models, libraries, and frameworks. Which model to choose, what embedding size, and how to approach tokenizing, are just some questions you are faced with when starting your work. We understood how many data scientists wanted an easier and more intuitive means to do their embedding work. This is why we built FastEmbed, a Python library engineered for speed, efficiency, and usability. We have created easy to use default workflows, handling the 80% use cases in NLP embedding.
diff --git a/qdrant-landing/content/articles/food-discovery-demo.md b/qdrant-landing/content/articles/food-discovery-demo.md
index d82039343..24efd559c 100644
--- a/qdrant-landing/content/articles/food-discovery-demo.md
+++ b/qdrant-landing/content/articles/food-discovery-demo.md
@@ -9,7 +9,7 @@ weight: -30
 author: Kacper Łukawski
 author_link: https://medium.com/@lukawskikacper
 date: 2023-09-05T11:32:00.000Z
-category: practicle-examples
+category: data-exploration
 ---
 
 Not every search journey begins with a specific destination in mind. Sometimes, you just want to explore and see what’s out there and what you might like.
diff --git a/qdrant-landing/content/articles/geo-polygon-filter-gsoc.md b/qdrant-landing/content/articles/geo-polygon-filter-gsoc.md
index 4afa54d35..3956488d3 100644
--- a/qdrant-landing/content/articles/geo-polygon-filter-gsoc.md
+++ b/qdrant-landing/content/articles/geo-polygon-filter-gsoc.md
@@ -15,7 +15,7 @@ keywords:
     - geo polygon
     - search condition
     - gsoc'23
-category: qdrant-internals
+hideFromList: true
 ---
 
 
diff --git a/qdrant-landing/content/articles/how-to-choose-an-embedding-model.md b/qdrant-landing/content/articles/how-to-choose-an-embedding-model.md
index 9104d90f4..12c0cd245 100644
--- a/qdrant-landing/content/articles/how-to-choose-an-embedding-model.md
+++ b/qdrant-landing/content/articles/how-to-choose-an-embedding-model.md
@@ -7,7 +7,7 @@ social_preview_image: /articles_data/how-to-choose-an-embedding-model/preview/so
 author: Kacper Łukawski
 author_link: https://www.kacperlukawski.com
 date: 2025-07-15T00:00:00.000Z
-category: vector-search-manuals
+category: core-concepts
 draft: false
 ---
 
diff --git a/qdrant-landing/content/articles/hybrid-search.md b/qdrant-landing/content/articles/hybrid-search.md
index b62d7f9c9..6fe7a09ff 100644
--- a/qdrant-landing/content/articles/hybrid-search.md
+++ b/qdrant-landing/content/articles/hybrid-search.md
@@ -8,7 +8,7 @@ weight: -150
 author: Kacper Łukawski
 author_link: https://kacperlukawski.com
 date: 2024-07-25T00:00:00.000Z
-category: vector-search-manuals
+category: mastering-search
 ---
 
 It's been over a year since we published the original article on how to build a hybrid
diff --git a/qdrant-landing/content/articles/indexing-optimization.md b/qdrant-landing/content/articles/indexing-optimization.md
index 2fbcd8259..67b5d362e 100644
--- a/qdrant-landing/content/articles/indexing-optimization.md
+++ b/qdrant-landing/content/articles/indexing-optimization.md
@@ -7,7 +7,7 @@ social_preview_image: /articles_data/indexing-optimization/preview/social_previe
 weight: -155
 author: Sabrina Aquino  
 date: 2025-02-13T00:00:00.000Z  
-category: vector-search-manuals
+category: production-ops
 ---
 
 # Optimizing Memory Consumption During Bulk Uploads
diff --git a/qdrant-landing/content/articles/langchain-integration.md b/qdrant-landing/content/articles/langchain-integration.md
index 7c7e3cb88..9aaca5849 100644
--- a/qdrant-landing/content/articles/langchain-integration.md
+++ b/qdrant-landing/content/articles/langchain-integration.md
@@ -18,7 +18,7 @@ keywords:
   - question answering
   - openai
   - embeddings
-category: practicle-examples
+category: demos-and-tutorials
 ---
 
 # Streamlining Question Answering: Simplifying Integration with LangChain and Qdrant
diff --git a/qdrant-landing/content/articles/late-interaction-models.md b/qdrant-landing/content/articles/late-interaction-models.md
index dc1e79b70..8c3ae9840 100644
--- a/qdrant-landing/content/articles/late-interaction-models.md
+++ b/qdrant-landing/content/articles/late-interaction-models.md
@@ -8,7 +8,7 @@ weight: -160
 author: Kacper Łukawski
 author_link: https://kacperlukawski.com
 date: 2024-08-14T00:00:00.000Z
-category: machine-learning
+category: embedding-research
 ---
 
 \* At least any open-source model, since you need access to its internals.
diff --git a/qdrant-landing/content/articles/machine-learning/_index.md b/qdrant-landing/content/articles/machine-learning/_index.md
deleted file mode 100644
index f1d8b4853..000000000
--- a/qdrant-landing/content/articles/machine-learning/_index.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-title: Machine Learning
-short_description: "Machine learning articles on embeddings, metric learning, fine-tuning, and applying neural retrieval with Qdrant."
-description: Explore Machine Learning principles and practices which make modern semantic similarity search possible. Apply Qdrant and vector search capabilities to your ML projects.
-category: machine-learning
-url: /articles/machine-learning/
-isCategoryPage: true
-weight: 40
----
diff --git a/qdrant-landing/content/articles/mastering-search/_index.md b/qdrant-landing/content/articles/mastering-search/_index.md
new file mode 100644
index 000000000..6e3c34216
--- /dev/null
+++ b/qdrant-landing/content/articles/mastering-search/_index.md
@@ -0,0 +1,9 @@
+---
+title: Mastering Search
+short_description: "Hands-on guides to building better search with Qdrant: filtering, hybrid search, multivectors, and the Query API."
+description: Go beyond the basics and master vector search with Qdrant. Learn how to combine filtering, hybrid retrieval, multivectors, and reranking to build high-quality search.
+category: mastering-search
+url: /articles/mastering-search/
+isCategoryPage: true
+weight: 20
+---
diff --git a/qdrant-landing/content/articles/memory-consumption.md b/qdrant-landing/content/articles/memory-consumption.md
index 01649639a..2579897d3 100644
--- a/qdrant-landing/content/articles/memory-consumption.md
+++ b/qdrant-landing/content/articles/memory-consumption.md
@@ -9,7 +9,7 @@ weight: 7
 author: Andrei Vasnetsov
 author_link: https://blog.vasnetsov.com/
 date: 2022-12-07T10:18:00.000Z
-category: qdrant-internals
+category: production-ops
 # aliases: [ /articles/memory-consumption/ ]
 ---
 
diff --git a/qdrant-landing/content/articles/metric-learning-tips.md b/qdrant-landing/content/articles/metric-learning-tips.md
index 93a3a3efa..7e587b8ff 100644
--- a/qdrant-landing/content/articles/metric-learning-tips.md
+++ b/qdrant-landing/content/articles/metric-learning-tips.md
@@ -10,7 +10,7 @@ weight: 20
 author: Andrei Vasnetsov
 author_link: https://blog.vasnetsov.com/
 date: 2021-05-15T10:18:00.000Z
-category: machine-learning
+category: embedding-research
 # aliases: [ /articles/metric-learning-tips/ ]
 ---
 
diff --git a/qdrant-landing/content/articles/miniCOIL.md b/qdrant-landing/content/articles/miniCOIL.md
index b6eabf722..3fa3598a6 100644
--- a/qdrant-landing/content/articles/miniCOIL.md
+++ b/qdrant-landing/content/articles/miniCOIL.md
@@ -13,7 +13,7 @@ keywords:
   - sparse retrieval
   - bm25
   - splade
-category: machine-learning
+category: embedding-research
 ---
 
 Have you ever heard of sparse neural retrieval? If so, have you used it in production? 
diff --git a/qdrant-landing/content/articles/modern-sparse-neural-retrieval.md b/qdrant-landing/content/articles/modern-sparse-neural-retrieval.md
index 76266e647..a0a3a9bd1 100644
--- a/qdrant-landing/content/articles/modern-sparse-neural-retrieval.md
+++ b/qdrant-landing/content/articles/modern-sparse-neural-retrieval.md
@@ -12,7 +12,7 @@ tags:
   - sparse retrieval
   - splade
   - bm25
-category: machine-learning
+category: embedding-research
 ---
 
 Finding enough time to study all the modern solutions while keeping your production running is rarely feasible.
diff --git a/qdrant-landing/content/articles/multitenancy.md b/qdrant-landing/content/articles/multitenancy.md
index 481914a19..5f4cc8dc4 100644
--- a/qdrant-landing/content/articles/multitenancy.md
+++ b/qdrant-landing/content/articles/multitenancy.md
@@ -14,7 +14,7 @@ keywords:
   - custom sharding
   - multiple partitions
   - vector database
-category: vector-search-manuals
+category: production-ops
 ---
 
 # Scaling Your Machine Learning Setup: The Power of Multitenancy and Custom Sharding in Qdrant 
diff --git a/qdrant-landing/content/articles/muvera-embeddings.md b/qdrant-landing/content/articles/muvera-embeddings.md
index c33f16077..af8ee4040 100644
--- a/qdrant-landing/content/articles/muvera-embeddings.md
+++ b/qdrant-landing/content/articles/muvera-embeddings.md
@@ -7,7 +7,7 @@ social_preview_image: /articles_data/muvera-embeddings/preview/social_preview.jp
 author: Kacper Łukawski
 author_link: https://kacperlukawski.com
 date: 2025-09-05T00:00:00.000Z
-category: vector-search-manuals
+category: mastering-search
 ---
 
 ## What are MUVERA Embeddings?
diff --git a/qdrant-landing/content/articles/neural-search-tutorial.md b/qdrant-landing/content/articles/neural-search-tutorial.md
index 7bc53028d..8cfea7a3a 100644
--- a/qdrant-landing/content/articles/neural-search-tutorial.md
+++ b/qdrant-landing/content/articles/neural-search-tutorial.md
@@ -10,7 +10,7 @@ weight: 50
 author: Andrey Vasnetsov
 author_link: https://blog.vasnetsov.com/
 date: 2021-06-10T10:18:00.000Z
-category: vector-search-manuals
+category: demos-and-tutorials
 # aliases: [ /articles/neural-search-tutorial/ ]
 ---
 # Neural Search 101: A Comprehensive Guide and Step-by-Step Tutorial
diff --git a/qdrant-landing/content/articles/practicle-examples/_index.md b/qdrant-landing/content/articles/practicle-examples/_index.md
deleted file mode 100644
index b5da8c131..000000000
--- a/qdrant-landing/content/articles/practicle-examples/_index.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-title: Practical Examples
-short_description: "Hands-on examples that show how to build production search and RAG features using Qdrant and vector embeddings."
-description: Building blocks and reference implementations to help you get started with Qdrant. Learn how to use Qdrant to solve real-world problems and build the next generation of AI applications.
-category: practicle-examples
-url: /articles/practicle-examples/
-isCategoryPage: true
-weight: 60
----
diff --git a/qdrant-landing/content/articles/product-quantization.md b/qdrant-landing/content/articles/product-quantization.md
index 70d21e851..cd8dc89fe 100644
--- a/qdrant-landing/content/articles/product-quantization.md
+++ b/qdrant-landing/content/articles/product-quantization.md
@@ -14,7 +14,7 @@ keywords:
   - vector search
   - product quantization
   - memory optimization
-category: qdrant-internals
+category: production-ops
 aliases: [ /articles/product_quantization/ ]
 ---
 
diff --git a/qdrant-landing/content/articles/production-ops/_index.md b/qdrant-landing/content/articles/production-ops/_index.md
new file mode 100644
index 000000000..8e94472ae
--- /dev/null
+++ b/qdrant-landing/content/articles/production-ops/_index.md
@@ -0,0 +1,9 @@
+---
+title: Production Ops
+short_description: "Running Qdrant in production: scaling, memory optimization, quantization, multitenancy, and access control."
+description: Operate Qdrant at scale. Learn how to optimize memory and resources, apply quantization, manage multitenancy and sharding, and secure access in production.
+category: production-ops
+url: /articles/production-ops/
+isCategoryPage: true
+weight: 40
+---
diff --git a/qdrant-landing/content/articles/qa-with-cohere-and-qdrant.md b/qdrant-landing/content/articles/qa-with-cohere-and-qdrant.md
index 33678d36e..cb919363b 100644
--- a/qdrant-landing/content/articles/qa-with-cohere-and-qdrant.md
+++ b/qdrant-landing/content/articles/qa-with-cohere-and-qdrant.md
@@ -16,7 +16,8 @@ keywords:
   - cohere
   - co.embed
   - embeddings
-category: practicle-examples
+category: demos-and-tutorials
+hideFromList: true
 ---
 
 Bi-encoders are probably the most efficient way of setting up a semantic Question Answering system. 
diff --git a/qdrant-landing/content/articles/qdrant-internals/_index.md b/qdrant-landing/content/articles/qdrant-internals/_index.md
index 753b33df9..4d368ca02 100644
--- a/qdrant-landing/content/articles/qdrant-internals/_index.md
+++ b/qdrant-landing/content/articles/qdrant-internals/_index.md
@@ -5,5 +5,5 @@ description: Take a look under the hood of Qdrant’s high-performance vector se
 category: qdrant-internals
 url: /articles/qdrant-internals/
 isCategoryPage: true
-weight: 20
+weight: 50
 ---
diff --git a/qdrant-landing/content/articles/rag-and-agents/_index.md b/qdrant-landing/content/articles/rag-and-agents/_index.md
new file mode 100644
index 000000000..fd7a78bc4
--- /dev/null
+++ b/qdrant-landing/content/articles/rag-and-agents/_index.md
@@ -0,0 +1,9 @@
+---
+title: RAG & Agents
+short_description: "Building RAG and agentic systems on Qdrant: agentic RAG, agent memory, semantic caching, and API access."
+description: Build retrieval-augmented generation and agentic applications with Qdrant. Learn agentic RAG patterns, agent memory, semantic caching, and how agents access your data.
+category: rag-and-agents
+url: /articles/rag-and-agents/
+isCategoryPage: true
+weight: 70
+---
diff --git a/qdrant-landing/content/articles/rag-and-genai/_index.md b/qdrant-landing/content/articles/rag-and-genai/_index.md
deleted file mode 100644
index 74730e96e..000000000
--- a/qdrant-landing/content/articles/rag-and-genai/_index.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-title: RAG & GenAI
-short_description: "Articles on retrieval-augmented generation and GenAI patterns built on Qdrant, from agentic RAG to hybrid retrieval."
-description: Leverage Qdrant for Retrieval-Augmented Generation (RAG) and build AI Agents
-category: rag-and-genai
-url: /articles/rag-and-genai/
-isCategoryPage: true
-weight: 50
----
diff --git a/qdrant-landing/content/articles/rag-is-dead.md b/qdrant-landing/content/articles/rag-is-dead.md
index 861acaecf..8b1d07993 100644
--- a/qdrant-landing/content/articles/rag-is-dead.md
+++ b/qdrant-landing/content/articles/rag-is-dead.md
@@ -15,7 +15,7 @@ keywords:
   - vector search
   - retrieval augmented generation
   - gemini 1.5
-category: rag-and-genai
+category: core-concepts
 ---
 
 # Is RAG Dead? The Role of Vector Databases in AI Efficiency and Vector Search 
diff --git a/qdrant-landing/content/articles/rapid-rag-optimization-with-qdrant-and-quotient.md b/qdrant-landing/content/articles/rapid-rag-optimization-with-qdrant-and-quotient.md
index 35977160e..87b890459 100755
--- a/qdrant-landing/content/articles/rapid-rag-optimization-with-qdrant-and-quotient.md
+++ b/qdrant-landing/content/articles/rapid-rag-optimization-with-qdrant-and-quotient.md
@@ -17,7 +17,7 @@ keywords:
 - quotient
 - optimization
 - rag
-category: rag-and-genai
+category: search-quality
 ---
 
 In today's fast-paced, information-rich world, AI is revolutionizing knowledge management. The systematic process of capturing, distributing, and effectively using knowledge within an organization is one of the fields in which AI provides exceptional value today. 
diff --git a/qdrant-landing/content/articles/relevance-feedback.md b/qdrant-landing/content/articles/relevance-feedback.md
index 45c9c079f..925876889 100644
--- a/qdrant-landing/content/articles/relevance-feedback.md
+++ b/qdrant-landing/content/articles/relevance-feedback.md
@@ -13,7 +13,7 @@ keywords:
   - reranking
   - query rewriting
   - relevance
-category: machine-learning
+category: search-quality
 ---
 
 A year ago, we dropped a statement-bomb in the “[Relevance Feedback in Information Retrieval](https://qdrant.tech/articles/search-feedback-loop/)” article and then went silent.
diff --git a/qdrant-landing/content/articles/scalar-quantization.md b/qdrant-landing/content/articles/scalar-quantization.md
index d0cde7e17..6a83ebd06 100644
--- a/qdrant-landing/content/articles/scalar-quantization.md
+++ b/qdrant-landing/content/articles/scalar-quantization.md
@@ -14,7 +14,7 @@ keywords:
   - vector search
   - scalar quantization
   - memory optimization
-category: qdrant-internals
+category: production-ops
 ---
 # Efficiency Unleashed: The Power of Scalar Quantization
 
diff --git a/qdrant-landing/content/articles/search-as-you-type.md b/qdrant-landing/content/articles/search-as-you-type.md
index b6156cb15..2091184bb 100644
--- a/qdrant-landing/content/articles/search-as-you-type.md
+++ b/qdrant-landing/content/articles/search-as-you-type.md
@@ -11,7 +11,7 @@ author_link: https://llogiq.github.io
 date: 2023-08-14T00:00:00+01:00
 draft: false
 keywords: search, semantic, vector, llm, integration, benchmark, recommend, performance, rust
-category: practicle-examples
+category: demos-and-tutorials
 ---
 
 Qdrant is one of the fastest vector search engines out there, so while looking for a demo to show off, we came upon the idea to do a search-as-you-type box with a fully semantic search backend. Now we already have a semantic/keyword hybrid search on our website. But that one is written in Python, which incurs some overhead for the interpreter. Naturally, I wanted to see how fast I could go using Rust.
diff --git a/qdrant-landing/content/articles/search-feedback-loop.md b/qdrant-landing/content/articles/search-feedback-loop.md
index 189baa706..dc4a12e1e 100644
--- a/qdrant-landing/content/articles/search-feedback-loop.md
+++ b/qdrant-landing/content/articles/search-feedback-loop.md
@@ -16,7 +16,7 @@ keywords:
     - lexical search
     - search
     - informational retrieval
-category: machine-learning
+category: search-quality
 ---
 
 > A problem well stated is a problem half solved.
diff --git a/qdrant-landing/content/articles/search-quality/_index.md b/qdrant-landing/content/articles/search-quality/_index.md
new file mode 100644
index 000000000..a542d4a6a
--- /dev/null
+++ b/qdrant-landing/content/articles/search-quality/_index.md
@@ -0,0 +1,9 @@
+---
+title: Search Quality
+short_description: "Measuring and improving search relevance with Qdrant: evaluation, relevance feedback, and benchmarking."
+description: Learn how to evaluate and improve the quality of your vector search. Explore relevance feedback, evaluation methodologies, and benchmarking techniques.
+category: search-quality
+url: /articles/search-quality/
+isCategoryPage: true
+weight: 30
+---
diff --git a/qdrant-landing/content/articles/semantic-cache-ai-data-retrieval.md b/qdrant-landing/content/articles/semantic-cache-ai-data-retrieval.md
index 3a2c2040a..7eecae8c6 100644
--- a/qdrant-landing/content/articles/semantic-cache-ai-data-retrieval.md
+++ b/qdrant-landing/content/articles/semantic-cache-ai-data-retrieval.md
@@ -18,7 +18,7 @@ tags:
   - AI applications
   - data retrieval
   - efficient data storage
-category: rag-and-genai
+category: rag-and-agents
 ---
 
 ## What is Semantic Cache?
diff --git a/qdrant-landing/content/articles/serverless.md b/qdrant-landing/content/articles/serverless.md
index 410957898..6cc23d7f9 100644
--- a/qdrant-landing/content/articles/serverless.md
+++ b/qdrant-landing/content/articles/serverless.md
@@ -11,7 +11,7 @@ author_link: https://llogiq.github.io
 date: 2023-07-12T10:00:00+01:00
 draft: false
 keywords: rust, serverless, lambda, semantic, search
-category: practicle-examples
+category: demos-and-tutorials
 ---
 
 Do you want to insert a semantic search function into your website or online app? Now you can do so - without spending any money! In this example, you will learn how to create a free prototype search engine for your own non-commercial purposes.
diff --git a/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-1.md b/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-1.md
index 55561aeca..be5b1056b 100644
--- a/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-1.md
+++ b/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-1.md
@@ -8,7 +8,7 @@ weight: -200
 author: Thierry Damiba
 author_link: https://github.com/thierrydamiba
 date: 2026-03-09T00:00:00.000Z
-category: practicle-examples
+category: mastering-search
 ---
 
 *This is Part 1 of a 5-part series on fine-tuning sparse embeddings for e-commerce search. We'll go from "why bother?" to a production system that beats BM25 by 29%.*
diff --git a/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-2.md b/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-2.md
index c22943c16..b8a75b199 100644
--- a/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-2.md
+++ b/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-2.md
@@ -8,7 +8,7 @@ weight: -199
 author: Thierry Damiba
 author_link: https://github.com/thierrydamiba
 date: 2026-03-09T00:00:00.000Z
-category: practicle-examples
+category: mastering-search
 ---
 
 *This is Part 2 of a 5-part series on fine-tuning sparse embeddings for e-commerce search. In [Part 1](/articles/sparse-embeddings-ecommerce-part-1/), we covered why sparse embeddings beat BM25 for e-commerce. Now we build the training pipeline.*
diff --git a/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-3.md b/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-3.md
index 710ed01c6..4500b1de1 100644
--- a/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-3.md
+++ b/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-3.md
@@ -8,7 +8,7 @@ weight: -198
 author: Thierry Damiba
 author_link: https://github.com/thierrydamiba
 date: 2026-03-09T00:00:00.000Z
-category: practicle-examples
+category: mastering-search
 ---
 
 *This is Part 3 of a 5-part series on fine-tuning sparse embeddings for e-commerce search. In [Part 2](/articles/sparse-embeddings-ecommerce-part-2/), we trained a SPLADE model on Modal. Now we evaluate it and push further with hard negative mining.*
diff --git a/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-4.md b/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-4.md
index 9ec751568..4395d7199 100644
--- a/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-4.md
+++ b/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-4.md
@@ -8,7 +8,7 @@ weight: -197
 author: Thierry Damiba
 author_link: https://github.com/thierrydamiba
 date: 2026-03-09T00:00:00.000Z
-category: practicle-examples
+category: mastering-search
 ---
 
 *This is Part 4 of a 5-part series on fine-tuning sparse embeddings for e-commerce search. In [Part 3](/articles/sparse-embeddings-ecommerce-part-3/), we evaluated our model and implemented hard negative mining. Now we test how well it generalizes.*
diff --git a/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-5.md b/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-5.md
index 468639dc5..8a1e3c1d4 100644
--- a/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-5.md
+++ b/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-5.md
@@ -8,7 +8,7 @@ weight: -196
 author: Thierry Damiba
 author_link: https://github.com/thierrydamiba
 date: 2026-03-09T00:00:00.000Z
-category: practicle-examples
+category: mastering-search
 ---
 
 *This is Part 5 of a series on fine-tuning sparse embeddings for e-commerce search. Parts [1](/articles/sparse-embeddings-ecommerce-part-1/)–[4](/articles/sparse-embeddings-ecommerce-part-4/) built the pipeline from scratch. This article packages it into a tool anyone can use.*
diff --git a/qdrant-landing/content/articles/sparse-vectors.md b/qdrant-landing/content/articles/sparse-vectors.md
index 11476377a..e49dd6ff3 100644
--- a/qdrant-landing/content/articles/sparse-vectors.md
+++ b/qdrant-landing/content/articles/sparse-vectors.md
@@ -15,7 +15,7 @@ keywords:
   - SPLADE
   - hybrid search
   - vector search
-category: vector-search-manuals
+category: core-concepts
 ---
 
 Think of a library with a vast index card system. Each index card only has a few keywords marked out (sparse vector) of a large possible set for each book (document). This is what sparse vectors enable for text. 
diff --git a/qdrant-landing/content/articles/storing-multiple-vectors-per-object-in-qdrant.md b/qdrant-landing/content/articles/storing-multiple-vectors-per-object-in-qdrant.md
index b01980455..2fa591040 100644
--- a/qdrant-landing/content/articles/storing-multiple-vectors-per-object-in-qdrant.md
+++ b/qdrant-landing/content/articles/storing-multiple-vectors-per-object-in-qdrant.md
@@ -14,7 +14,7 @@ tags:
   - Database
   - Search
   - Similarity Search
-category: vector-search-manuals
+category: mastering-search
 ---
 
 # How to Optimize Vector Storage by Storing Multiple Vectors Per Object
diff --git a/qdrant-landing/content/articles/triplet-loss.md b/qdrant-landing/content/articles/triplet-loss.md
index 317a1f8d4..4960ebbdd 100644
--- a/qdrant-landing/content/articles/triplet-loss.md
+++ b/qdrant-landing/content/articles/triplet-loss.md
@@ -9,7 +9,7 @@ weight: 30
 author: Yusuf Sarıgöz
 author_link: https://medium.com/@yusufsarigoz
 date: 2022-03-24T15:12:00+03:00
-category: machine-learning
+category: embedding-research
 # aliases: [ /articles/triplet-loss/ ]
 ---
 
diff --git a/qdrant-landing/content/articles/vector-search-filtering.md b/qdrant-landing/content/articles/vector-search-filtering.md
index 7b4ccc773..20b7ecd2b 100644
--- a/qdrant-landing/content/articles/vector-search-filtering.md
+++ b/qdrant-landing/content/articles/vector-search-filtering.md
@@ -8,7 +8,7 @@ weight: -200
 author: Sabrina Aquino, David Myriel
 author_link: 
 date: 2024-09-10T00:00:00.000Z
-category: vector-search-manuals
+category: mastering-search
 ---
 Imagine you sell computer hardware. To help shoppers easily find products on your website, you need to have a **user-friendly [search engine](https://qdrant.tech)**.
 
diff --git a/qdrant-landing/content/articles/vector-search-manuals/_index.md b/qdrant-landing/content/articles/vector-search-manuals/_index.md
deleted file mode 100644
index 8095972b3..000000000
--- a/qdrant-landing/content/articles/vector-search-manuals/_index.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-title: Vector Search Manuals
-short_description: "Practical manuals for vector search with Qdrant: indexing, filtering, hybrid retrieval, quantization, and scaling tips."
-description: Take full control of your vector data with Qdrant. Learn how to easily store, organize, and optimize vectors for high-performance similarity search.
-category: vector-search-manuals
-url: /articles/vector-search-manuals/
-isCategoryPage: true
-weight: 10
----
diff --git a/qdrant-landing/content/articles/vector-search-production.md b/qdrant-landing/content/articles/vector-search-production.md
index 85bb61dd0..582b4667b 100644
--- a/qdrant-landing/content/articles/vector-search-production.md
+++ b/qdrant-landing/content/articles/vector-search-production.md
@@ -7,7 +7,7 @@ social_preview_image: /articles_data/vector-search-production/social_preview.png
 author: David Myriel
 author_link: 
 date: 2025-04-30T00:00:00.000Z
-category: vector-search-manuals
+category: production-ops
 ---
 
 ## What Does it Take to Run Search in Production?
diff --git a/qdrant-landing/content/articles/vector-search-resource-optimization.md b/qdrant-landing/content/articles/vector-search-resource-optimization.md
index 0b20ed337..d469fd984 100644
--- a/qdrant-landing/content/articles/vector-search-resource-optimization.md
+++ b/qdrant-landing/content/articles/vector-search-resource-optimization.md
@@ -6,7 +6,7 @@ preview_dir: /articles_data/vector-search-resource-optimization/preview
 social_preview_image: /articles_data/vector-search-resource-optimization/preview/social_preview.jpg
 weight: -200
 author: David Myriel
-category: vector-search-manuals
+category: production-ops
 date: 2025-02-09T00:00:00.000Z
 ---
 
diff --git a/qdrant-landing/content/articles/web-ui-gsoc.md b/qdrant-landing/content/articles/web-ui-gsoc.md
index 0ce5d4a35..97de9ddab 100644
--- a/qdrant-landing/content/articles/web-ui-gsoc.md
+++ b/qdrant-landing/content/articles/web-ui-gsoc.md
@@ -17,7 +17,7 @@ keywords:
     - vector similarity
     - exploration
     - recommendation
-category: ecosystem
+hideFromList: true
 ---
 
 
diff --git a/qdrant-landing/content/articles/what-are-embeddings.md b/qdrant-landing/content/articles/what-are-embeddings.md
index 21b97e3a4..da6d9a55a 100644
--- a/qdrant-landing/content/articles/what-are-embeddings.md
+++ b/qdrant-landing/content/articles/what-are-embeddings.md
@@ -18,7 +18,7 @@ tags:
   - embeddings
   - machine-learning
   - artificial intelligence
-category: vector-search-manuals
+category: core-concepts
 ---
 
 > **Embeddings** are numerical machine learning representations of the semantic of the input data. They capture the meaning of complex, high-dimensional data, like text, images, or audio, into vectors. Enabling algorithms to process and analyze the data more efficiently.
diff --git a/qdrant-landing/content/articles/what-is-a-vector-database.md b/qdrant-landing/content/articles/what-is-a-vector-database.md
index de2507ea5..90f645143 100644
--- a/qdrant-landing/content/articles/what-is-a-vector-database.md
+++ b/qdrant-landing/content/articles/what-is-a-vector-database.md
@@ -15,7 +15,7 @@ tags:
   - vector-search
   - vector-database
   - embeddings
-category: vector-search-manuals
+category: core-concepts
 ---
 
 ## An Introduction to Vector Databases
diff --git a/qdrant-landing/content/articles/what-is-quantization.md b/qdrant-landing/content/articles/what-is-quantization.md
index baf2de1fc..a75c22a42 100644
--- a/qdrant-landing/content/articles/what-is-quantization.md
+++ b/qdrant-landing/content/articles/what-is-quantization.md
@@ -17,7 +17,7 @@ tags:
   - product quantization
   - scalar quantization
   - vector compression
-category: vector-search-manuals
+category: core-concepts
 ---
 
 Vector quantization is a data compression technique used to reduce the size of high-dimensional data. Compressing vectors reduces memory usage while maintaining nearly all of the essential information. This method allows for more efficient storage and faster search operations, particularly in large datasets.
diff --git a/qdrant-landing/content/articles/what-is-rag-in-ai.md b/qdrant-landing/content/articles/what-is-rag-in-ai.md
index 808f9b587..3b5d443af 100644
--- a/qdrant-landing/content/articles/what-is-rag-in-ai.md
+++ b/qdrant-landing/content/articles/what-is-rag-in-ai.md
@@ -18,7 +18,7 @@ tags:
   - embeddings
   - llm rag
   - rag application
-category: rag-and-genai
+category: core-concepts
 ---
 
 > Retrieval-augmented generation (RAG) integrates external information retrieval into the process of generating responses by Large Language Models (LLMs). It searches a database for information beyond its pre-trained knowledge base, significantly improving the accuracy and relevance of the generated responses.
diff --git a/qdrant-landing/content/learn/ref-acticles-vector-search-manuals.md b/qdrant-landing/content/learn/ref-acticles-core-concepts.md
similarity index 81%
rename from qdrant-landing/content/learn/ref-acticles-vector-search-manuals.md
rename to qdrant-landing/content/learn/ref-acticles-core-concepts.md
index b2b50783a..70b4a01c9 100644
--- a/qdrant-landing/content/learn/ref-acticles-vector-search-manuals.md
+++ b/qdrant-landing/content/learn/ref-acticles-core-concepts.md
@@ -1,11 +1,11 @@
 ---
 #Delimiter files are used to separate the list of documentation pages into sections.
 type: reference
-reference: /articles/vector-search-manuals
+reference: /articles/core-concepts
 weight: 110
 sitemapExclude: True
 build:
   publishResources: false
   render: never
 partition: learn
----
\ No newline at end of file
+---
diff --git a/qdrant-landing/content/learn/ref-acticles-data-exploration.md b/qdrant-landing/content/learn/ref-acticles-data-exploration.md
index 5250ec467..5f048000f 100644
--- a/qdrant-landing/content/learn/ref-acticles-data-exploration.md
+++ b/qdrant-landing/content/learn/ref-acticles-data-exploration.md
@@ -2,7 +2,7 @@
 #Delimiter files are used to separate the list of documentation pages into sections.
 type: reference
 reference: /articles/data-exploration
-weight: 130
+weight: 180
 sitemapExclude: True
 build:
   publishResources: false
diff --git a/qdrant-landing/content/learn/ref-acticles-demos-and-tutorials.md b/qdrant-landing/content/learn/ref-acticles-demos-and-tutorials.md
new file mode 100644
index 000000000..c3f5c110b
--- /dev/null
+++ b/qdrant-landing/content/learn/ref-acticles-demos-and-tutorials.md
@@ -0,0 +1,11 @@
+---
+#Delimiter files are used to separate the list of documentation pages into sections.
+type: reference
+reference: /articles/demos-and-tutorials
+weight: 190
+sitemapExclude: True
+build:
+  publishResources: false
+  render: never
+partition: learn
+---
diff --git a/qdrant-landing/content/learn/ref-acticles-ecosystem.md b/qdrant-landing/content/learn/ref-acticles-ecosystem.md
deleted file mode 100644
index e8e07e48f..000000000
--- a/qdrant-landing/content/learn/ref-acticles-ecosystem.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-type: reference
-reference: /articles/ecosystem
-weight: 170
-sitemapExclude: True
-build:
-  publishResources: false
-  render: never
-partition: learn
----
\ No newline at end of file
diff --git a/qdrant-landing/content/learn/ref-acticles-embedding-research.md b/qdrant-landing/content/learn/ref-acticles-embedding-research.md
new file mode 100644
index 000000000..603e5a92a
--- /dev/null
+++ b/qdrant-landing/content/learn/ref-acticles-embedding-research.md
@@ -0,0 +1,11 @@
+---
+#Delimiter files are used to separate the list of documentation pages into sections.
+type: reference
+reference: /articles/embedding-research
+weight: 160
+sitemapExclude: True
+build:
+  publishResources: false
+  render: never
+partition: learn
+---
diff --git a/qdrant-landing/content/learn/ref-acticles-mastering-search.md b/qdrant-landing/content/learn/ref-acticles-mastering-search.md
new file mode 100644
index 000000000..729f37439
--- /dev/null
+++ b/qdrant-landing/content/learn/ref-acticles-mastering-search.md
@@ -0,0 +1,11 @@
+---
+#Delimiter files are used to separate the list of documentation pages into sections.
+type: reference
+reference: /articles/mastering-search
+weight: 120
+sitemapExclude: True
+build:
+  publishResources: false
+  render: never
+partition: learn
+---
diff --git a/qdrant-landing/content/learn/ref-acticles-practicle-examples.md b/qdrant-landing/content/learn/ref-acticles-practicle-examples.md
deleted file mode 100644
index 94e780afa..000000000
--- a/qdrant-landing/content/learn/ref-acticles-practicle-examples.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-type: reference
-reference: /articles/practicle-examples
-weight: 160
-sitemapExclude: True
-build:
-  publishResources: false
-  render: never
-partition: learn
----
\ No newline at end of file
diff --git a/qdrant-landing/content/learn/ref-acticles-machine-learning.md b/qdrant-landing/content/learn/ref-acticles-production-ops.md
similarity index 83%
rename from qdrant-landing/content/learn/ref-acticles-machine-learning.md
rename to qdrant-landing/content/learn/ref-acticles-production-ops.md
index 4ce472cae..da8426507 100644
--- a/qdrant-landing/content/learn/ref-acticles-machine-learning.md
+++ b/qdrant-landing/content/learn/ref-acticles-production-ops.md
@@ -1,11 +1,11 @@
 ---
 #Delimiter files are used to separate the list of documentation pages into sections.
 type: reference
-reference: /articles/machine-learning
+reference: /articles/production-ops
 weight: 140
 sitemapExclude: True
 build:
   publishResources: false
   render: never
 partition: learn
----
\ No newline at end of file
+---
diff --git a/qdrant-landing/content/learn/ref-acticles-qdrant-internals.md b/qdrant-landing/content/learn/ref-acticles-qdrant-internals.md
index 0eeb1fc68..0be7fac2b 100644
--- a/qdrant-landing/content/learn/ref-acticles-qdrant-internals.md
+++ b/qdrant-landing/content/learn/ref-acticles-qdrant-internals.md
@@ -2,7 +2,7 @@
 #Delimiter files are used to separate the list of documentation pages into sections.
 type: reference
 reference: /articles/qdrant-internals
-weight: 120
+weight: 150
 sitemapExclude: True
 build:
   publishResources: false
diff --git a/qdrant-landing/content/learn/ref-acticles-rag-and-genai.md b/qdrant-landing/content/learn/ref-acticles-rag-and-agents.md
similarity index 78%
rename from qdrant-landing/content/learn/ref-acticles-rag-and-genai.md
rename to qdrant-landing/content/learn/ref-acticles-rag-and-agents.md
index f87a38dd7..032585f70 100644
--- a/qdrant-landing/content/learn/ref-acticles-rag-and-genai.md
+++ b/qdrant-landing/content/learn/ref-acticles-rag-and-agents.md
@@ -1,11 +1,11 @@
 ---
 #Delimiter files are used to separate the list of documentation pages into sections.
 type: reference
-reference: /articles/rag-and-genai
-weight: 150
+reference: /articles/rag-and-agents
+weight: 170
 sitemapExclude: True
 build:
   publishResources: false
   render: never
 partition: learn
----
\ No newline at end of file
+---
diff --git a/qdrant-landing/content/learn/ref-acticles-search-quality.md b/qdrant-landing/content/learn/ref-acticles-search-quality.md
new file mode 100644
index 000000000..2593bb09b
--- /dev/null
+++ b/qdrant-landing/content/learn/ref-acticles-search-quality.md
@@ -0,0 +1,11 @@
+---
+#Delimiter files are used to separate the list of documentation pages into sections.
+type: reference
+reference: /articles/search-quality
+weight: 130
+sitemapExclude: True
+build:
+  publishResources: false
+  render: never
+partition: learn
+---
diff --git a/qdrant-landing/static/_redirects b/qdrant-landing/static/_redirects
index 2d01facae..e93ab6ae1 100644
--- a/qdrant-landing/static/_redirects
+++ b/qdrant-landing/static/_redirects
@@ -68,3 +68,10 @@
 /documentation/hybrid-search/   /documentation/search/hybrid-queries/    301
 /documentation/scroll/           /documentation/manage-data/points/#scroll-points       301
 /documentation/discovery/        /documentation/search/explore/           301
+
+# Articles category reorganization (technical articles taxonomy)
+/articles/vector-search-manuals/   /articles/mastering-search/      301
+/articles/machine-learning/        /articles/embedding-research/    301
+/articles/ecosystem/               /articles/demos-and-tutorials/   301
+/articles/practicle-examples/      /articles/demos-and-tutorials/   301
+/articles/rag-and-genai/           /articles/rag-and-agents/        301