Merge pull request #1199 from qdrant/maddie-seo-1

[SEO] Maddie updates 1

qdrant-landing/content/blog/hybrid-cloud-airbyte.md

@@ -13,7 +13,7 @@ tags:
   - Vector Database
 ---
 
-In their mission to support large-scale AI innovation, [Airbyte](https://airbyte.com/) and Qdrant are collaborating on the launch of Qdrant’s new offering - [Qdrant Hybrid Cloud](/hybrid-cloud/). This collaboration allows users to leverage the synergistic capabilities of both Airbyte and Qdrant within a private infrastructure. Qdrant’s new offering represents the first managed vector database that can be deployed in any environment. Businesses optimizing their data infrastructure with Airbyte are now able to host a vector database either on premise, or on a public cloud of their choice - while still reaping the benefits of a managed database product.
+In their mission to support large-scale AI innovation, [Airbyte](https://airbyte.com/) and Qdrant are collaborating on the launch of Qdrant’s new offering - [Qdrant Hybrid Cloud](/hybrid-cloud/). This collaboration allows users to leverage the synergistic capabilities of both Airbyte and Qdrant within a private infrastructure. Qdrant’s new offering represents the first managed [vector database](/articles/what-is-a-vector-database/) that can be deployed in any environment. Businesses optimizing their data infrastructure with Airbyte are now able to host a vector database either on premise, or on a public cloud of their choice - while still reaping the benefits of a managed database product.
 
 This is a major step forward in offering enterprise customers incredible synergy for maximizing the potential of their AI data. Qdrant's new Kubernetes-native design, coupled with Airbyte’s powerful data ingestion pipelines meet the needs of developers who are both prototyping and building production-level apps. Airbyte simplifies the process of data integration by providing a platform that connects to various sources and destinations effortlessly. Moreover, Qdrant Hybrid Cloud leverages advanced indexing and search capabilities to empower users to explore and analyze their data efficiently.
 

qdrant-landing/content/blog/hybrid-cloud-haystack.md

@@ -13,7 +13,7 @@ tags:
   - Vector Database
 ---
 
-We’re excited to share that Qdrant and [Haystack](https://haystack.deepset.ai/) are continuing to expand their seamless integration to the new [Qdrant Hybrid Cloud](/hybrid-cloud/) offering, allowing developers to deploy a managed vector database in their own environment of choice. Earlier this year, both Qdrant and Haystack, started to address their user’s growing need for production-ready retrieval-augmented-generation (RAG) deployments. The ability to build and deploy AI apps anywhere now allows for complete data sovereignty and control. This gives large enterprise customers the peace of mind they need before they expand AI functionalities throughout their operations.
+We’re excited to share that Qdrant and [Haystack](https://haystack.deepset.ai/) are continuing to expand their seamless integration to the new [Qdrant Hybrid Cloud](/hybrid-cloud/) offering, allowing developers to deploy a managed [vector database](/articles/what-is-a-vector-database/) in their own environment of choice. Earlier this year, both Qdrant and Haystack, started to address their user’s growing need for production-ready retrieval-augmented-generation (RAG) deployments. The ability to build and deploy AI apps anywhere now allows for complete data sovereignty and control. This gives large enterprise customers the peace of mind they need before they expand AI functionalities throughout their operations.
 
 With a highly customizable framework like Haystack, implementing vector search becomes incredibly simple. Qdrant's new Qdrant Hybrid Cloud offering and its Kubernetes-native design supports customers all the way from a simple prototype setup to a production scenario on any hosting platform. Users can attach AI functionalities to their existing in-house software by creating custom integration components. Don’t forget, both products are open-source and highly modular!
 

qdrant-landing/content/blog/semantic-cache-ai-data-retrieval.md

@@ -23,7 +23,7 @@ tags:
 
 **Semantic cache** is a method of retrieval optimization, where similar queries instantly retrieve the same appropriate response from a knowledge base. 
 
-Semantic cache differs from traditional caching methods. In computing, **cache** refers to high-speed memory that efficiently stores frequently accessed data. In the context of vector databases, a **semantic cache** improves AI application performance by storing previously retrieved results along with the conditions under which they were computed. This allows the application to reuse those results when the same or similar conditions occur again, rather than finding them from scratch.
+Semantic cache differs from traditional caching methods. In computing, **cache** refers to high-speed memory that efficiently stores frequently accessed data. In the context of [vector databases](/articles/what-is-a-vector-database/), a **semantic cache** improves AI application performance by storing previously retrieved results along with the conditions under which they were computed. This allows the application to reuse those results when the same or similar conditions occur again, rather than finding them from scratch.
 
 > The term **"semantic"** implies that the cache takes into account the meaning or semantics of the data or computation being cached, rather than just its syntactic representation. This can lead to more efficient caching strategies that exploit the structure or relationships within the data or computation.