diff --git a/content/develop/ai/search-and-query/_index.md b/content/develop/ai/search-and-query/_index.md
index 14d1fff327..7e87e5385f 100644
--- a/content/develop/ai/search-and-query/_index.md
+++ b/content/develop/ai/search-and-query/_index.md
@@ -46,10 +46,7 @@ Here are the next steps to get you started:
 1. Learn how to [create an index]({{< relref "/develop/ai/search-and-query/indexing/" >}}).
 1. Learn how to [query your data]({{< relref "/develop/ai/search-and-query/query/" >}}).
 1. [Install Redis Insight]({{< relref "/operate/redisinsight" >}}), connect it to your Redis database, and then use [Redis Copilot]({{< relref "/develop/tools/insight" >}}#redis-copilot) to help you learn how to execute complex queries against your own data using simple, plain language prompts.
-
-{{< alert title="Try it out" >}}
-Experiment with Redis Search interactively in the [Redis playground](https://redis.io/try/sandbox) — no installation required.
-{{< /alert >}}
+1. Open the [AI agent builder]({{< relref "/develop/ai/agent-builder" >}}) and choose the **Knowledge Assistant** template to generate a working RAG agent built on Redis vector search.
 
 {{< tip >}}
 See Redis vector search in a real workflow: [Redis Repo Memory](https://github.com/marketplace/actions/redis-repo-memory) is a GitHub Action that surfaces related past PRs, issues, and commits on every pull request. Add it to any repository in a few minutes.
@@ -63,6 +60,10 @@ See
 [Install Redis Software]({{< relref "/operate/rs/installing-upgrading/install" >}})
 for full installation instructions.
 
+{{< alert title="Try it out" >}}
+Experiment with Redis Search interactively in the [Redis playground](https://redis.io/try/sandbox) — no installation required.
+{{< /alert >}}
+
 ## License and source code
 
 The Redis Search features of Redis are available under the Source Available License 2.0 (RSALv2), the Server Side Public License v1 (SSPLv1), or the GNU Affero General Public License version 3 (AGPLv3). Please read the [license file](https://raw.githubusercontent.com/RediSearch/RediSearch/master/LICENSE.txt) for further details. The source code and the [detailed release notes](https://github.com/RediSearch/RediSearch/releases) are available on [GitHub](https://github.com/RediSearch/RediSearch).
diff --git a/content/develop/ai/search-and-query/query/vector-search.md b/content/develop/ai/search-and-query/query/vector-search.md
index a07adef715..1ba93a59c7 100644
--- a/content/develop/ai/search-and-query/query/vector-search.md
+++ b/content/develop/ai/search-and-query/query/vector-search.md
@@ -21,6 +21,10 @@ This article gives you a good overview of how to perform vector search queries w
 
 A vector search query on a vector field allows you to find all vectors in a vector space that are close to a given vector. You can query for the k-nearest neighbors or vectors within a given radius.
 
+{{< tip >}}
+To generate a complete RAG agent that uses the vector search queries described here, open the [AI agent builder]({{< relref "/develop/ai/agent-builder" >}}) and choose the **Knowledge Assistant** template.
+{{< /tip >}}
+
 The examples in this article use a schema with the following fields:
 
 | JSON field               | Field alias | Field type  | Description |
diff --git a/content/develop/ai/search-and-query/vectors/_index.md b/content/develop/ai/search-and-query/vectors/_index.md
index 92e85baa98..88e3f033fe 100644
--- a/content/develop/ai/search-and-query/vectors/_index.md
+++ b/content/develop/ai/search-and-query/vectors/_index.md
@@ -22,7 +22,7 @@ weight: 8
 
 Redis includes a [high-performance vector database](https://redis.io/blog/benchmarking-results-for-vector-databases/) that lets you perform semantic searches over vector embeddings. You can augment these searches with filtering over text, numerical, geospatial, and tag metadata.
 
-To quickly get started, check out the [Redis vector quickstart guide]({{< relref "develop/get-started/search-tutorial/vector-search" >}}) and the [Redis AI Resources](https://github.com/redis-developer/redis-ai-resources) Github repo.
+To quickly get started, check out the [Redis vector quickstart guide]({{< relref "develop/get-started/search-tutorial/vector-search" >}}) and the [Redis AI Resources](https://github.com/redis-developer/redis-ai-resources) Github repo. To generate a working example, open the [AI agent builder]({{< relref "/develop/ai/agent-builder" >}}) and choose the **Knowledge Assistant** template.
 
 {{< alert title="See vector search in action" >}}
 [Redis Repo Memory](https://github.com/marketplace/actions/redis-repo-memory) is a GitHub Action that gives your repository a memory: on every pull request it embeds the change and uses Redis KNN vector search to surface semantically related past PRs, issues, and commits — a concise, real-world example of the concepts on this page. See the [source on GitHub](https://github.com/redis-learn/redis-repo-memory).
diff --git a/content/develop/get-started/rag.md b/content/develop/get-started/rag.md
index 590c8539aa..8e93e94369 100644
--- a/content/develop/get-started/rag.md
+++ b/content/develop/get-started/rag.md
@@ -29,6 +29,10 @@ RAG involves three main steps:
 RAG enables LLMs to use real-time information, improving the accuracy and relevance of generated content.
 Redis is ideal for RAG due to its speed, versatility, and familiarity.
 
+{{< tip >}}
+To generate a complete RAG agent with document ingestion, hybrid retrieval, and citations, open the [AI agent builder]({{< relref "/develop/ai/agent-builder" >}}) and choose the **Knowledge Assistant** template.
+{{< /tip >}}
+
 ### The role of Redis in RAG
 
 Redis provides a robust platform for managing real-time data. It supports the storage and retrieval of vectors, essential for handling large-scale, unstructured data and performing similarity searches. Key features and components of Redis that make it suitable for RAG include: