Revise agent builder callouts to Google technical style

Drop the rhetorical "Want.../Prefer..." hooks and marketing phrasing,
lead with the task/goal, and use direct present-tense imperative.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Author: mich-elle-luna

content/develop/ai/search-and-query/_index.md

@@ -46,7 +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.
-1. Want working code to start from? Open the [AI agent builder]({{< relref "/develop/ai/agent-builder" >}}) and choose the **Knowledge Assistant** template to generate a complete RAG agent built on Redis vector search.
+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.
 
 {{< alert title="Try it out" >}}
 Experiment with Redis Search interactively in the [Redis playground](https://redis.io/try/sandbox) — no installation required.

content/develop/ai/search-and-query/query/vector-search.md

@@ -22,7 +22,7 @@ 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 >}}
-Want a working example instead of building from scratch? Open the [AI agent builder]({{< relref "/develop/ai/agent-builder" >}}) and choose the **Knowledge Assistant** template to generate a complete RAG agent that uses the vector search queries described here.
+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:

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. For a ready-to-run example, open the [AI agent builder]({{< relref "/develop/ai/agent-builder" >}}) and choose the **Knowledge Assistant** template.
+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).

content/develop/get-started/rag.md

@@ -30,7 +30,7 @@ RAG enables LLMs to use real-time information, improving the accuracy and releva
 Redis is ideal for RAG due to its speed, versatility, and familiarity.
 
 {{< tip >}}
-Prefer to start from working code? Open the [AI agent builder]({{< relref "/develop/ai/agent-builder" >}}) and choose the **Knowledge Assistant** template — it generates a complete RAG agent with document ingestion, hybrid retrieval, and citations.
+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