fix ai paths

Author: jacobbednarz

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

@@ -37,7 +37,7 @@ Cloudflare's AI platform has been designed with these principles in mind. The ar
 ![Figure 1: Composable AI architecture](~/assets/images/reference-architecture/ai-composable/ai-composable.svg "Figure 1: Composable AI architecture")
 
 1. **Compute**: The compute layer is the core of the application. All business logic, as well as use of other components, is defined here. The compute layer interacts with other services such as inference services, vector search, databases and data storage. Serverless solutions such as [Cloudflare Workers](/workers/) offer fast iteration and automatic scaling, which allows developers to focus on the use case instead of infrastructure management. Importantly for composability is the support of standard interfaces such as HTTP or TCP, which the Workers' runtime both supports via the [`fetch()` API](/workers/runtime-apis/fetch/) and [`connect()` API](/workers/runtime-apis/tcp-sockets/) respectively.
-2. **Inference**: AI inference is responsible for the AI-capabilities of the application. Operational models vary between self-hosting models or consuming Inference-as-a-service providers such as [Workers AI](/workers-ai/). In the latter case, [REST APIs](/api/resources/workers/subresources/ai/methods/run/) make interacting with inference services from any service/client easy to implement. Using platform-specific integrations such as [Bindings](/workers-ai/configuration/bindings/) for interaction between Workers and other services enable simplified development as complexity such as authentication is abstracted away.
+2. **Inference**: AI inference is responsible for the AI-capabilities of the application. Operational models vary between self-hosting models or consuming Inference-as-a-service providers such as [Workers AI](/workers-ai/). In the latter case, [REST APIs](/api/resources/ai/methods/run/) make interacting with inference services from any service/client easy to implement. Using platform-specific integrations such as [Bindings](/workers-ai/configuration/bindings/) for interaction between Workers and other services enable simplified development as complexity such as authentication is abstracted away.
 3. **Vector Search**: Certain use cases such as [RAG](/reference-architecture/diagrams/ai/ai-rag/) leverage vector search for similarity matching. Operational models vary between self-hosting databases or consuming vector-specific database-as-a-service (DBaaS) providers such as [Vectorize](/vectorize/). In the latter case, [REST APIs](/api/resources/vectorize/subresources/indexes/methods/list/) make interacting with it from any service/client easy to implement. Using platform-specific integrations such as [Bindings](/vectorize/get-started/embeddings/#3-bind-your-worker-to-your-index) for interaction between Workers and other services enable simplified development as complexity such as authentication is abstracted away.
 4. **Data & Storage**: Databases and data storage add state to AI applications. User management, session storage and persisting data are common requirements for AI applications. Depending on the use case, different solutions are required such as relationship databases or object storage. A variety of solutions for self-hosted or managed services exist. On Cloudflare, this could be for instance [D1](/d1/) and [R2](/r2/). REST APIs make interacting with inference services from any service/client easy to implement. Using platform-specific integrations such as Bindings for interaction between Workers and data and database services enable simplified development as complexity such as authentication is abstracted away.
 

src/content/docs/workers-ai/api-reference.mdx

@@ -1,7 +1,7 @@
 ---
 pcx_content_type: navigation
 title: REST API reference
-external_link: /api/resources/workers/subresources/ai/methods/run/
+external_link: /api/resources/ai/methods/run/
 sidebar:
   order: 9
 

src/content/docs/workers-ai/get-started/rest-api.mdx

@@ -42,7 +42,7 @@ If you choose to [create an API token](/fundamentals/api/get-started/create-toke
 
 After creating your API token, authenticate and make requests to the API using your API token in the request.
 
-You will use the [Execute AI model](/api/resources/workers/subresources/ai/methods/run/) endpoint to run the [`@cf/meta/llama-3.1-8b-instruct`](/workers-ai/models/llama-3.1-8b-instruct/) model:
+You will use the [Execute AI model](/api/resources/ai/methods/run/) endpoint to run the [`@cf/meta/llama-3.1-8b-instruct`](/workers-ai/models/llama-3.1-8b-instruct/) model:
 
 ```bash
 curl https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/@cf/meta/llama-3.1-8b-instruct \

src/content/docs/workers-ai/index.mdx

@@ -20,7 +20,7 @@ Run machine learning models, powered by serverless GPUs, on Cloudflare's global
 
 <Plan type="workers-all" />
 
-Workers AI allows you to run machine learning models, on the Cloudflare network, from your own code -- whether that be from [Workers](/workers/), [Pages](/pages/), or anywhere via [the Cloudflare API](/api/resources/workers/subresources/ai/methods/run/).
+Workers AI allows you to run machine learning models, on the Cloudflare network, from your own code -- whether that be from [Workers](/workers/), [Pages](/pages/), or anywhere via [the Cloudflare API](/api/resources/ai/methods/run/).
 
 With the launch of Workers AI, Cloudflare is rolling out GPUs to its global network. This enables you to build and deploy ambitious AI applications that run near your users, wherever they are.