sap-tutorials
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@@ -59,7 +59,7 @@ There are two key approaches to managing prompts in a **Prompt Registry**:
 1.	**Imperative API (Direct API Control for Dynamic Prompt Management)**: The Imperative API allows you to create, update, and manage prompt templates dynamically via API calls. This approach is best suited for interactive design-time use cases, where you need to iteratively refine prompts and track their versions. Each change is explicitly made via CRUD operations, and you can manage versions manually.
 2.	**Declarative API (Git-based Sync for Automated Prompt Management)**: The Declarative API, on the other hand, integrates with SAP AI Core applications and is ideal for CI/CD pipelines. Instead of managing templates through direct API interactions, you define them as YAML files in a Git repository. The system automatically syncs these templates, ensuring that updates are seamlessly reflected in the prompt registry without manual intervention.
 
-### You will learn:
+**You will learn:**
 
 - Declarative approach to creating a prompt template. 
 - How to **register and sync** a prompt template in Generative AI Hub.
 
@@ -56,7 +56,9 @@ You will learn how to:
 - Use relevant SAP AI Core features and configurations to support these capabilities.
 
 By the end of this tutorial
+
   * you'll understand how to design a secure and controlled orchestration pipeline suitable for **enterprise-grade GenAI applications**.
+  
   * Learn how to implement the solution using **SAP AI Launchpad**, **Python SDK**, **Java**, **JavaScript**, and **Bruno**.
 
 ### Accessing Orchestration Capabilities
@@ -188,7 +190,7 @@ System.out.println(cvContent);
 [OPTION END]
 
 
-### Configuring User-Specific Settings  
+### Template Configuration  
 
 [OPTION BEGIN [AI Launchpad]]
 
 
@@ -41,12 +41,14 @@ You will learn how to deploy and configure orchestration to enable the consumpti
 
 We will walk through a **step-by-step guide** and demonstrate the orchestration flow using a **resume processing use case**. This real-world scenario highlights how different models can collaborate within a cohesive pipeline using orchestration.
 
-> **Note:** In SAP AI Core, orchestration is provided by default with the creation of the default resource group. For any new or additional resource groups, you must deploy a separate orchestration setup.
+> **Note:** In SAP AI Core, orchestration deployment is available by default in the default resource group during the onboarding. For any new or additional resource groups, you must deploy a separate orchestration setup.
 
 While orchestration in SAP AI Core offers capabilities such as **data masking, content filtering, translation, and grounding**, this tutorial focuses on the basic consumption flow using mandatory modules like **templating** and **model configuration**. Others modules are Optional and usage of those modules are covered in a separate tutorial.
 
-By the end of this tutorial, 
+By the end of this tutorial,
+
    * you will have a foundational understanding of orchestration through its minimal usage, focusing on practical application of templates and how to switch between different models using harmonized APIs.
+
    * Learn how to implement the solution using **SAP AI Launchpad**, **Python SDK**, **Java**, **JavaScript**, and **Bruno**.
 
    Refer to the [orchestration documentation](https://help.sap.com/docs/sap-ai-core/sap-ai-core-service-guide/orchestration-8d022355037643cebf775cd3bf662cc5?locale=en-US&version=CLOUD) for more information.