fix docs and images

qdrant-landing/content/case-studies/bloop.md

@@ -1,7 +1,7 @@
 ---
-title: "Qdrant case study: bloop semantic code search"
-short_description: bloop is a fast code-search engine that combines semantic search, regex search and precise code navigation
-description: bloop is a fast code-search engine that combines semantic search, regex search and precise code navigation
+title: "Powering Bloop semantic code search"
+short_description: Bloop is a fast code-search engine that combines semantic search, regex search and precise code navigation
+description: Bloop is a fast code-search engine that combines semantic search, regex search and precise code navigation
 social_preview_image: /articles_data/bloop/social_preview.png
 preview_dir: /articles_data/bloop/preview
 weight: 2

qdrant-landing/content/case-studies/pienso.md

@@ -1,15 +1,13 @@
 ---
-title: "How to Design and Scale Your Own ML Platform"
-short_description: Lessons from Pienso on building domain-specific foundation models.
-description: Lessons from Pienso on building domain-specific foundation models.
+title: "Design and Scale Your Own ML Platform: Lessons from Pienso"
+short_description: Why Pienso chose Qdrant as a cornerstone for building domain-specific foundation models.
+description: Why Pienso chose Qdrant as a cornerstone for building domain-specific foundation models.
 social_preview_image: /case-studies/pienso/social_preview.png
 preview_dir: /case-studies/pienso/preview
 weight: 1
 ---
 
-## Lessons from Pienso on building domain-specific foundation models
-
-### Why vector databases more than ever
+## Leveraging vector databases
 
 Perhaps the most valuable application of vector databases is their unique capability to improve the accuracy of foundation models, or large language models (LLMs), particularly in terms of reducing hallucinations and unfounded claims generated by these models. This advantage is brought into focus when considering the relationship between vector databases and the extensive context windows managed by advanced LLMs. A vector database serves as a robust reservoir of high-dimensional vectors, mathematical representations of features, attributes or relationships laden in text data. By tapping into these vectors, LLMs produce concrete, database-backed responses, rather than leaning solely on patterns identified during the training phase. This results in more grounded, factual responses that resist the tendency for hallucination.
 
@@ -21,7 +19,7 @@ The utility of vector databases is enhanced when combined with the large context
 
 For instance, when a model with large context windows encounters a prompt, it can scan the vector database for pertinent vectors associated with the input and its context. It identifies relevant vectors based on similarity measures such as cosine similarity, and then utilizes this information to guide the text generation process. This system blends the data-backed accuracy provided by the vector database with the contextual sensitivity offered by the large context window, ensuring more factual, contextually relevant, and consistent output, which significantly reduces the possibility of hallucinations.
 
-### Why Pienso chose Qdrant
+## Why Pienso chose Qdrant
 
 After a thorough evaluation of other high-performing vector databases, Pienso selected Qdrant for its best-in-class LLM interoperability. Our partnership makes it easier for commercial organizations to realize the potential of large language models and interactive deep learning, and trust the results they generate.
 
@@ -37,7 +35,7 @@ Compared to other vector databases, Qdrant stood out in four distinctive areas w
 
 4. **Memmap**: Vector databases, or vector search engines as they’re sometimes called, are designed specifically for storing and searching high-dimensional vector data, a form of data representation common in machine learning models. This focus makes them an indispensable tool for achieving efficient and rapid retrieval in systems that engage with large-scale machine learning models. __Qdrant supports memmap storage, a feature that provides fast performance comparable to in-memory storage. This capability is critical in a machine learning context where rapid data access and retrieval are required for training and inference tasks.__
 
-### Taking It to Market: 
+## Taking it to Market 
 
 How will building with a high-performing, open-source vector database benefit our customers and strengthen our value proposition with partners?
 
@@ -51,7 +49,7 @@ How will building with a high-performing, open-source vector database benefit ou
 
 4. **Code-Level Data Persistence Enhances Data Safety and Restorability:** Thanks to Qdrant's implementation in Rust — a language celebrated for its memory safety guarantees — users can expect robust data protection. Additionally, Qdrant employs write-ahead logging (WAL), ensuring that changes are safely logged before being applied to the database, providing additional layers of data safety.
 
-### Delivering model autonomous generative AI to enterprise users
+## Delivering model autonomous generative AI to enterprise users
 
 The partnership between Pienso and Qdrant delivers a combination of no-code/low-code interactive deep learning with efficient vector computation engineered for open source models and libraries.