add meta descriptions

:

Author: kodster28

src/content/docs/ai-gateway/tutorials/deploy-aig-worker.mdx

@@ -9,6 +9,7 @@ products:
   - Workers
 languages:
   - JavaScript
+description: Learn how to deploy a Worker that makes calls to OpenAI through AI Gateway
 ---
 
 import { Render, PackageManagers } from "~/components";

src/content/docs/cloudflare-for-platforms/workers-for-platforms/demos.mdx

@@ -3,10 +3,13 @@ pcx_content_type: navigation
 title: Demos and architectures
 sidebar:
   order: 8
-
 ---
 
-import { ExternalResources, GlossaryTooltip, ResourcesBySelector } from "~/components"
+import {
+	ExternalResources,
+	GlossaryTooltip,
+	ResourcesBySelector,
+} from "~/components";
 
 Learn how you can use Workers for Platforms within your existing architecture.
 
@@ -20,4 +23,11 @@ Explore the following <GlossaryTooltip term="demo application">demo applications
 
 Explore the following <GlossaryTooltip term="reference architecture">reference architectures</GlossaryTooltip> that use Workers:
 
-<ResourcesBySelector types={["reference-architecture","design-guide","reference-architecture-diagram"]} products={["WorkersForPlatforms"]} />
+<ResourcesBySelector
+	types={[
+		"reference-architecture",
+		"design-guide",
+		"reference-architecture-diagram",
+	]}
+	products={["workers-for-platforms"]}
+/>

src/content/docs/d1/tutorials/build-a-staff-directory-app/index.mdx

@@ -10,6 +10,7 @@ tags:
 languages:
   - TypeScript
   - SQL
+description: Build a staff directory using D1. Users access employee info; admins add new employees within the app.
 ---
 
 import { WranglerConfig } from "~/components";

src/content/docs/developer-spotlight/tutorials/creating-a-recommendation-api.mdx

@@ -19,6 +19,7 @@ tags:
   - Stripe
 sidebar:
   order: 2
+description: Create APIs for related product searches and recommendations using Workers AI and Stripe.
 ---
 
 import {

src/content/docs/pages/tutorials/build-a-blog-using-nuxt-and-sanity/index.mdx

@@ -8,6 +8,7 @@ tags:
   - Vue
 languages:
   - JavaScript
+description: Build a blog application using Nuxt.js and Sanity.io and deploy it on Cloudflare Pages.
 ---
 
 import { Stream, PackageManagers } from "~/components";

src/content/docs/reference-architecture/diagrams/ai/ai-asset-creation.mdx

@@ -9,12 +9,9 @@ sidebar:
   order: 1
   label: Content-based asset creation
 updated: 2024-03-18
+description: AI systems combine text-generation and text-to-image models to create visual content from text. They generate prompts, moderate content, and produce images for various applications.
 ---
 
-#### Reference Architecture Diagram
-
-## Introduction
-
 Combining text-generation models with text-to-image models can lead to powerful AI systems capable of generating visual content based on input prompts. This integration can be achieved through a collaborative framework where a text-generation model generates prompts for the text-to-image model based on input text.
 
 Here's how the process can work:

src/content/docs/reference-architecture/diagrams/ai/ai-rag.mdx

@@ -14,10 +14,9 @@ sidebar:
   order: 1
   label: Retrieval Augmented Generation (RAG)
 updated: 2024-03-18
+description: RAG combines retrieval with generative models for better text. It uses external knowledge to create factual, relevant responses, improving coherence and accuracy in NLP tasks like chatbots.
 ---
 
-## Introduction
-
 Retrieval-Augmented Generation (RAG) is an innovative approach in natural language processing that integrates retrieval mechanisms with generative models to enhance text generation.
 
 By incorporating external knowledge from pre-existing sources, RAG addresses the challenge of generating contextually relevant and informative text. This integration enables RAG to overcome the limitations of traditional generative models by ensuring that the generated text is grounded in factual information and context. RAG aims to solve the problem of information overload by efficiently retrieving and incorporating only the most relevant information into the generated text, leading to improved coherence and accuracy. Overall, RAG represents a significant advancement in NLP, offering a more robust and contextually aware approach to text generation.

src/content/docs/reference-architecture/diagrams/ai/bigquery-workers-ai.mdx

@@ -14,10 +14,9 @@ sidebar:
   order: 1
   label: Ingesting BigQuery Data into Workers AI
 updated: 2024-10-26
+description: You can connect a Cloudflare Worker to get data from Google BigQuery and pass it to Workers AI, to run AI Models, powered by serverless GPUs.
 ---
 
-## Introduction
-
 You can connect a Cloudflare Worker to get data from Google BigQuery and pass it to Workers AI, to run AI Models, powered by serverless GPUs. This will allow you to enhance data with AI-generated responses, such as detecting the sentiment score of some text or generating tags for an article. This document describes a simple way to get started if you are looking to give Workers AI a try and see how the [new and different AI models](/workers-ai/models/) would perform with your data hosted in BigQuery.
 
 ## User-based approach

src/content/docs/reference-architecture/diagrams/content-delivery/optimizing-image-delivery-with-cloudflare-image-resizing-and-r2.mdx

@@ -10,6 +10,7 @@ sidebar:
   order: 1
   label: Optimizing image delivery
 updated: 2024-06-12
+description: Learn how to get a scalable, high-performance solution to optimizing image delivery.
 ---
 
 ## Introduction

src/content/docs/reference-architecture/diagrams/iot/optimizing-and-securing-connected-transportation-systems.mdx

@@ -22,10 +22,9 @@ sidebar:
   order: 1
   label: Connected transportation systems
 updated: 2024-12-02
+description: This diagram showcases Cloudflare components optimizing connected transportation systems. It illustrates how their technologies minimize latency, ensure reliability, and strengthen security for critical data flow.
 ---
 
-## Introduction
-
 A connected transport system is an integrated network of vehicles, infrastructure, and/or services that rely on constant data exchange in real-time to improve safety, efficiency, and mobility. Examples include public transportation (buses, trams, and trains), emergency vehicles (ambulances, fire trucks, and police cars), fleet management systems (logistics and delivery trucks), autonomous vehicles, connected infrastructure (traffic lights, road signs), platooning systems (truck convoys), drone delivery vehicles, and connected cars. They can be broadly categorized into:
 
 - **Fixed location devices**: Systems such as CCTV cameras, traffic signals, and roadside sensors that remain in fixed locations and push data through a central gateway.