docs(app): minor formatting

docs/docs/tutorials/cookbooks/AI-powered-code-reviews.mdx

@@ -15,7 +15,7 @@ Ever wanted your own AI assistant to review pull requests? In this tutorial, we'
 [You can try out the final product here](https://7acthbzemz2hgvi8l7us455b6tcigd8s.vercel.app/). Just provide the URL to a public PR and receive a review from our AI assistant.
 
 <Image
-  style={{ display: "block", margin: "10 auto" }}
+  style={{ display: "block", margin: "10px auto" }}
   img={require("/images/cookbooks/ai-powered-code-reviews/code-review-demo.gif")}
   alt="Code review demo"
   loading="lazy"
@@ -138,7 +138,7 @@ To set up Agenta, we need to set the environment variable `AGENTA_API_KEY` (whic
 We can now run the app and see the traces in Agenta.
 
 <Image
-  style={{ display: "block", margin: "10 auto" }}
+  style={{ display: "block", margin: "10px auto" }}
   img={require("/images/cookbooks/ai-powered-code-reviews/observability-pr.gif")}
   alt="Code review demo"
   loading="lazy"
@@ -257,7 +257,7 @@ We can now add the application to Agenta. Here's what we need to do:
 The last command builds and serves your application, making it accessible through Agenta's playground. There, you can run the application end-to-end by giving it a PR URL and getting the review generated by our LLM.
 
 <Image
-  style={{ display: "block", margin: "10 auto" }}
+  style={{ display: "block", margin: "10px auto" }}
   img={require("/images/cookbooks/ai-powered-code-reviews/pr-playground.png")}
   alt="Code review demo"
   loading="lazy"
@@ -274,7 +274,7 @@ Next, we'll set up an LLM-as-a-judge to evaluate the quality of the reviews.
 To do this, navigate to the evaluation view, click on "Configure evaluators", then "Create new evaluator" and select "LLM-as-a-judge".
 
 <Image
-  style={{ display: "block", margin: "10 auto" }}
+  style={{ display: "block", margin: "10px auto" }}
   img={require("/images/cookbooks/ai-powered-code-reviews/pr-configure.png")}
   alt="Code review demo"
   loading="lazy"
@@ -314,7 +314,7 @@ LLM APP OUTPUT: {prediction}
 Note that the evaluator accesses the LLM app's output through the `{prediction}` variable. We can iterate on the prompt and test different models in the evaluator test view.
 
 <Image
-  style={{ display: "block", margin: "10 auto" }}
+  style={{ display: "block", margin: "10px auto" }}
   img={require("/images/cookbooks/ai-powered-code-reviews/pr-human-eval.png")}
   alt="Code review demo"
   loading="lazy"
@@ -323,7 +323,7 @@ Note that the evaluator accesses the LLM app's output through the `{prediction}`
 With our evaluator set up, we can run experiments and compare different prompts and models. In the playground, we can create multiple variants and run batch evaluations using the `pr-review-quality` LLM-as-a-judge.
 
 <Image
-  style={{ display: "block", margin: "10 auto" }}
+  style={{ display: "block", margin: "10px auto" }}
   img={require("/images/cookbooks/ai-powered-code-reviews/pr-run-eval.png")}
   alt="Code review demo"
   loading="lazy"
@@ -340,7 +340,7 @@ Deployment is straightforward with Agenta:
 3. Select "Deploy to Production"
 
 <Image
-  style={{ display: "block", margin: "10 auto" }}
+  style={{ display: "block", margin: "10px auto" }}
   img={require("/images/cookbooks/ai-powered-code-reviews/pr-deploy.png")}
   alt="Code review demo"
   loading="lazy"
@@ -349,7 +349,7 @@ Deployment is straightforward with Agenta:
 This gives you an API endpoint ready to use in your application.
 
 <Image
-  style={{ display: "block", margin: "10 auto" }}
+  style={{ display: "block", margin: "10px auto" }}
   img={require("/images/cookbooks/ai-powered-code-reviews/pr-prod.png")}
   alt="Code review demo"
   loading="lazy"