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Co-authored-by: ToriLindsay <[email protected]>
Co-authored-by: Kody Jackson <[email protected]>

Author: 3 people

src/content/docs/autorag/configuration/system-prompt.mdx

@@ -17,7 +17,7 @@ A system prompt is a special instruction sent to a large language model (LLM) th
 System prompts are particularly useful for:
 
 - Enforcing specific response formats
-- Constraining behavior (e.g., only respond based on provided content)
+- Constraining behavior (for example, it only responds based on the provided content)
 - Applying domain-specific tone or terminology
 - Encouraging consistent, high-quality output
 

src/content/docs/autorag/index.mdx

@@ -26,6 +26,8 @@ AutoRAG lets you create fully-managed, retrieval-augmented generation (RAG) pipe
 
 <Plan type="all" />
 
+<Plan type="all" />
+
 You can use AutoRAG to build:
 
 - **Chatbots:** Improve chatbot accuracy by providing relevant up-to-date information.

src/content/docs/autorag/usage/workers-binding.mdx

@@ -16,7 +16,7 @@ import {
 	Type,
 } from "~/components";
 
-Cloudflare’s serverless platform allows you to run code at the edge to build full-stack applications with Workers. A binding enables your Worker or Pages Function to interact with resources on the Cloudflare Developer Platform.
+Cloudflare’s serverless platform allows you to run code at the edge to build full-stack applications with [Workers](/workers/). A [binding](/workers/runtime-apis/bindings/) enables your Worker or Pages Function to interact with resources on the Cloudflare Developer Platform.
 
 To use your AutoRAG with Workers or Pages, create an AI binding either in the Cloudflare dashboard (refer to [AI bindings](/pages/functions/bindings/#workers-ai) for instructions), or you can update your [Wrangler file](/workers/wrangler/configuration/). To bind AutoRAG to your Worker, add the following to your Wrangler file:
 

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

@@ -27,7 +27,7 @@ Examples for application of these technique includes for instance customer servi
 In the context of Retrieval-Augmented Generation (RAG), knowledge seeding involves incorporating external information from pre-existing sources into the generative process, while querying refers to the mechanism of retrieving relevant knowledge from these sources to inform the generation of coherent and contextually accurate text. Both are shown below.
 
 :::note[Looking for a managed option?]
-[AutoRAG](/autorag) offers a fully managed way to build RAG pipelines on Cloudflare, handling ingestion, indexing, and querying out of the box. [Get started](/autorag/get-started/).
+[AutoRAG](/autorag) offers a fully managed way to build RAG pipelines on Cloudflare, handling ingestion, indexing, and querying out of the box. [Get started with AutoRAG](/autorag/get-started/).
 :::
 
 ## Knowledge Seeding

src/content/docs/vectorize/index.mdx

@@ -50,7 +50,7 @@ Learn how to use Vectorize to generate vector embeddings using Workers AI.
 
 </Feature>
 
-<Feature header="Search using Vectorize and AutoRAG" href="/autorag" cta="Build a RAG with Vectorize">
+<Feature header="Search using Vectorize and AutoRAG" href="/autorag/" cta="Build a RAG with Vectorize">
 
 Learn how to automatically index your data and store it in Vectorize, then query it to generate context-aware responses using AutoRAG.
 

src/content/docs/vectorize/reference/what-is-a-vector-database.mdx

@@ -55,7 +55,7 @@ When a user initiates a prompt, instead of passing it (without additional contex
 3. These vectors are used to look up the content they relate to (if not embedded directly alongside the vectors as metadata).
 4. This content is provided as context alongside the original user prompt, providing additional context to the LLM and allowing it to return an answer that is likely to be far more contextual than the standalone prompt.
 
-Create a RAG today with [AutoRAG](/autorag) to deploy a fully managed RAG pipeline in just a few clicks. It automatically sets up Vectorize, handles continuous indexing, and serves responses through a single API.
+Create a RAG today with [AutoRAG](/autorag) to deploy a fully managed RAG pipeline in just a few clicks. AutoRAG automatically sets up Vectorize, handles continuous indexing, and serves responses through a single API.
 
 <sup>1</sup> You can learn more about the theory behind RAG by reading the [RAG
 paper](https://arxiv.org/abs/2005.11401).

src/content/products/autorag.yaml

@@ -13,6 +13,4 @@ meta:
   author: '@cloudflare'
 
 resources:
-    community: 
-    dashboard_link:
     discord: https://discord.gg/cloudflaredev

src/pages/index.astro

@@ -72,7 +72,7 @@ const topCards = [
 	{
 		title: "AI Products",
 		links: [
-			{ text: "AutoRAG", href: "/autorag" },
+			{ text: "AutoRAG", href: "/autorag/" },
 			{ text: "Workers AI", href: "/workers-ai/" },
 			{ text: "Vectorize", href: "/vectorize/" },
 			{ text: "AI Gateway", href: "/ai-gateway/" },