diff --git a/docs/.nav.yml b/docs/.nav.yml
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--- a/docs/.nav.yml
+++ b/docs/.nav.yml
@@ -15,7 +15,10 @@ nav:
       - Lineage Tracking: "register-and-refine/lineage-tracking.md"
       - Prompt Optimization: "register-and-refine/prompt-optimization.md"
       - Corridor Sync: "register-and-refine/corridor_sync.md"
-
+      - Asset Registration Examples: 
+            - Model Registration: "register-and-refine/example/model.md"
+            - Prompt Registration: "register-and-refine/example/prompt.md"
+            
   - Evaluate and Approve:
       - Overview: "evaluate-and-approve/index.md"
       - Simulation: "evaluate-and-approve/simulation.md"
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+# Model Registration: Gemini 2.0 Flash
+
+This guide covers registering the Gemini 2.0 Flash model on the platform. 
+
+**Gemini 2.0 Flash** is Google's language model for classification and structured output tasks.
+
+---
+
+## Registration Steps
+
+### Step 1. Navigate to Model Catalog
+
+Go to **GenAI Studio → Model Catalog** and click the **Create** button.
+
+### Step 2. Fill in Basic Information
+
+![alt text](image-21.png)
+
+**Basic Information** fields help organize and identify your model:
+
+- **Name:** Human-readable identifier for the model (e.g., "Gemini 2.0 Flash")
+- **Description:** Brief explanation of the model's purpose and capabilities
+- **Group:** Category for organizing similar models together (e.g., "Foundation LLMs")
+- **Permissible Purpose:** Approved use cases and business scenarios for this model
+- **Ownership Type:** License type - Proprietary, Open Source, or Internal
+- **Model Type:** Classification of the model (e.g., "LLM" for language models)
+
+### Step 3. Configure Inferencing Logic 
+
+#### Choose Input Type
+
+**Input Type:** You have two options:
+
+- **API Based** - Use this when working with models through API providers (OpenAI, Anthropic, Google Vertex AI, etc.)
+
+- **Python Function** - Use this for custom Python implementations or local models
+
+For this guide, we'll use **API Based**.
+
+#### Select Model Provider
+
+**Model Provider:** Select `Google Vertex AI` from the dropdown
+
+Once you select a provider, additional fields will appear to configure how the model is called:
+
+![alt text](image-22.png)
+
+- **Alias:** Variable name to reference this model in pipeline code (e.g., `gemini_2_0_flash`)
+- **Output Type:** Data type returned by the model (e.g., `dict[str, str]`)
+- **Input Type:** Choose between API-based (for external providers) or Python Function (for custom code)
+- **Model Provider:** Select the API provider hosting the model (Google Vertex AI)
+- **Model:** Specific model version from the provider's catalog (Gemini 2.0 Flash)
+
+#### Define Arguments
+
+The inputs to the model - messages, system instruction, temperature, etc.
+
+Click **+ Add Argument** to add each argument:
+
+| Alias | Type | Is Optional | Default Value |
+|-------|------|-------------|---------------|
+| `text` | String | ☐ | - |
+| `temperature` | Numerical | ☑ | 0 |
+| `system_instruction` | String | ☑ | None |
+
+**Argument Descriptions:**
+
+- `text`: The input prompt to send to the model
+
+- `temperature`: Controls randomness (0 = deterministic, 1 = creative)
+
+- `system_instruction`: Optional system-level instructions for the model
+
+You can add additional arguments based on your model's requirements.
+
+#### Write Scoring Logic
+
+![alt text](image-23.png)
+
+Provide logic to initialize and score the model:
+
+```python
+import os
+from google import genai
+from google.genai import types
+
+client = genai.Client(api_key=os.getenv("GOOGLE_API_TOKEN"))
+
+config = types.GenerateContentConfig(
+    temperature=temperature, 
+    seed=2025, 
+    system_instruction=system_instruction
+)
+
+response = client.models.generate_content(
+    model="gemini-2.0-flash", 
+    contents=text, 
+    config=config
+)
+
+return {
+    "response": response.text,
+}
+```
+
+**What This Code Does:**
+
+- Authenticates using the `GOOGLE_API_TOKEN` environment variable (configured in Platform Integrations)
+- Sets up generation config with temperature and system instruction
+- Calls the Gemini 2.0 Flash model with the input text
+- Returns the generated response
+
+### Step 4. Save the Model
+
+Add any notes or additional information in the **Additional Information** section, then click **Create** to complete registration.
+
+
+### Step 5. Quick Example Run 
+
+Click **Test Code** to run a sample query.
+
+![alt text](image-19.png)
+
+Use the platform's test interface to verify:
+
+- Verify API authentication is working
+- Test with sample inputs before using in production
+- Debug any configuration issues
+- Validate the output format matches expectations
+
+## Usage in Pipelines
+
+Once registered, the model appears in your Resources library and can be selected for any downstream usages.
+
+**Reference in pipeline code:**
+```python
+# Call the registered model
+response = gemini_2_0_flash(
+    text=user_prompt,
+    temperature=0.7,
+    system_instruction="You are a helpful assistant."
+)
+
+# Access the response
+output_text = response["response"]
+```
+
+---
+
+## Related Documentation
+
+- [Prompt Registration Guide](../prompt/) - Create reusable prompts
+- [Google Gemini API Docs](https://ai.google.dev/gemini-api/docs) - Official Google documentation
+
+---
diff --git a/docs/register-and-refine/example/prompt.md b/docs/register-and-refine/example/prompt.md
new file mode 100644
index 0000000..e3ab80a
--- /dev/null
+++ b/docs/register-and-refine/example/prompt.md
@@ -0,0 +1,312 @@
+# Prompt Registration Guide
+
+This guide covers how to register prompts on the Corridor platform, using an **Intent Classification Prompt** as a working example.
+
+If you are new to Prompts, then this doc might help you understanding what they are and how do they work -> [Prompts](/docs/register-and-refine/inventory-management/prompts/index.md)
+
+---
+
+## Registration Steps
+
+### Step 1. Navigate to Prompt Registry
+
+Go to **GenAI Studio → Prompt Registry** and click the **Create** button.
+
+### Step 2. Fill in Basic Information
+
+**Example for Intent Classification:**
+
+![alt text](image.png)
+
+**Basic Information** fields help organize and identify your prompt:
+
+- **Description:** Clear explanation of what the prompt does and its purpose
+- **Group:** Category for organizing similar prompts (e.g., "Existing Customer Credit Card Related Prompts")
+- **Permissible Purpose:** Approved use cases and business scenarios for this prompt
+- **Task Type:** Classification of the prompt's function (e.g., "Classification" for intent detection)
+- **Prompt Type:** Format of the prompt (e.g., "System Instruction" for system-level prompts)
+- **Prompt Elements:** Optional tags or metadata for additional categorization
+
+### Step 3. Configure Prompt Template
+
+![alt text](image-1.png)
+
+**Alias:** `customer_intent_classification_prompt`
+
+- A Python variable name to reference this prompt in pipelines
+
+#### Example Prompt Template
+
+The **Prompt Template** is where you write the actual instructions for the LLM:
+
+- Use `{}` placeholders for dynamic variables (e.g., `{customer_utterance}`)
+- Write clear, structured instructions for the model to follow
+- Include examples to guide the model's behavior
+- Define expected output format (e.g., JSON schema)
+
+**Example Prompt Template for Intent Classification:**
+
+````markdown
+# PERSONA & TONE
+
+You are a trusted, efficient, and security-conscious digital assistant,
+specialized in handling banking-related queries for existing customers
+of BankX.
+
+Maintain a tone that is:
+
+- Professional: Clear, formal, and polite
+- Concise: Direct answers without filler
+- Data-driven: Never guess; respond only based on verified data
+- English only
+
+# GOAL
+
+Accurately predict customer intent from a predefined list of possible intents.
+
+# TASK INSTRUCTIONS:
+
+### Step 1: Review Intent Definitions
+
+Thoroughly understand the predefined list of intents.
+
+### Step 2: Pre-Defined List of Intents
+
+#### ACTIVATE CARD
+
+- Definition: Request to activate a newly issued card
+- Examples:
+  • "How do I activate my new debit card?"
+  • "Activate my credit card now."
+
+#### BLOCK CARD
+
+- Definition: Request to block lost, stolen, or compromised card
+- Examples:
+  • "Block my credit card immediately."
+  • "I lost my debit card, can you block it?"
+
+#### CARD DETAILS
+
+- Definition: Inquiry about card information
+- Examples:
+  • "How many cards do I have?"
+  • "What is the name on my card?"
+
+#### CHECK CARD ANNUAL FEE
+
+- Definition: Inquiry about annual fees
+- Examples:
+  • "What's the annual fee for my credit card?"
+  • "How much is my card's yearly charge?"
+
+#### CHECK CURRENT BALANCE ON CARD
+
+- Definition: Inquiry about available balance
+- Examples:
+  • "What's my credit card balance?"
+  • "How much money is on my debit card?"
+
+### Step 3: Disambiguate and Summarize Customer Utterance
+
+- Overlook grammatical/spelling errors
+- Ignore PII (name, age, gender, personal data)
+- Focus on main intention in long sentences
+
+### Step 4: Mapping Query to Intent
+
+- Map to most suitable intent from predefined list
+- Ensure only one intent is chosen
+- Recheck classification is in predefined list
+
+### Step 5: Schema Compliance
+
+OUTPUT FORMAT:
+
+```json
+{{"classified_intent": "str"}}
+```
+
+# EXAMPLE SCENARIOS:
+
+Example 1:
+Input: "I need to activate my new credit card."
+
+    REASONING STEPS:
+    - Review intent definitions
+    - Understand all available intents
+    - No disambiguation needed (clear query)
+    - Maps to "ACTIVATE CARD" intent
+    - Output in JSON format
+
+    Output:
+
+```json
+    {{"classified_intent": "ACTIVATE CARD"}}
+```
+
+# Customer Query
+
+Query: {customer_utterance}
+````
+
+#### Define Arguments
+
+Arguments are inputs that get passed into the prompt template.
+
+Click **+ Add Argument** to add:
+
+| Alias          | Type   | Is Optional | Default Value |
+| -------------- | ------ | ----------- | ------------- |
+| `user_message` | String | ☐ No        | -             |
+
+**Note:** Use `{customer_utterance}` in the template and map it from `user_message` in Prompt Creation Logic.
+
+### Step 4. Write Prompt Creation Logic
+
+**Prompt Creation Logic** allows you to programmatically process arguments before they're inserted into the template. This is useful for:
+
+- Formatting complex data structures
+- Generating dynamic content (like the intent list)
+- Applying conditional logic based on inputs
+- Validating or transforming user inputs
+
+**Example - Formatting Intent Definitions:**
+
+![alt text](image-2.png)
+
+```python
+intent_definitions = [
+    {
+        "Intent": "ACTIVATE CARD",
+        "Definition": "Request to activate a newly issued card",
+        "Examples": [
+            "How do I activate my new debit card?",
+            "Activate my credit card now.",
+        ],
+    },
+    {
+        "Intent": "BLOCK CARD",
+        "Definition": "Request to block a lost, stolen, or compromised card",
+        "Examples": [
+            "Block my credit card immediately.",
+            "I lost my debit card, can you block it?",
+        ],
+    },
+    {
+        "Intent": "CARD DETAILS",
+        "Definition": "Inquiry about card information",
+        "Examples": [
+            "How many cards do I have?",
+            "What is the name on my card?",
+        ],
+    },
+    {
+        "Intent": "CHECK CARD ANNUAL FEE",
+        "Definition": "Inquiry about annual fees",
+        "Examples": [
+            "What's the annual fee for my credit card?",
+            "How much is my card's yearly charge?",
+        ],
+    },
+    {
+        "Intent": "CHECK CURRENT BALANCE ON CARD",
+        "Definition": "Inquiry about available balance",
+        "Examples": [
+            "What's my credit card balance?",
+            "How much money is on my debit card?",
+        ],
+    },
+]
+
+def get_intent_info(data_list):
+    """Format intent definitions into readable text"""
+    formatted_list = []
+    intent_number = 1
+
+    for item in data_list:
+        formatted_list.append(f"#### {intent_number}. {item['Intent'].upper()}")
+        formatted_list.append(f"- Definition: {item['Definition']}")
+        formatted_list.append(f"- Examples:")
+        for example in item["Examples"]:
+            formatted_list.append(f"  • {example}")
+        formatted_list.append("")  # Empty line between intents
+        intent_number += 1
+
+    return "\n".join(formatted_list)
+
+# Fill in the prompt template
+return prompt.format(
+    customer_utterance=user_message,
+    list_of_intents=get_intent_info(intent_definitions)
+)
+```
+
+**What This Does:**
+
+1. Defines 5 card-related intent definitions with examples
+2. Formats them into a structured, numbered list
+3. Fills in `{customer_utterance}` and `{list_of_intents}` placeholders
+
+### Step 5. Save the Prompt
+
+Click **Create** to register the prompt.
+
+The prompt is now:
+
+- Available in the Prompt Registry
+- Usable in pipelines and other objects
+
+### Analyze and Improve the Prompt using GGX Capability
+
+After saving the prompt, you can test and refine it directly within **GenAI Studio**:
+
+- **🔍 Analyze Prompt:**  
+  Click the **Analyze Prompt** button to evaluate how your prompt behaves with different inputs.  
+  This helps you confirm that argument mappings, placeholders, and output formats are working correctly.
+
+- **✨ Improve with AI:**  
+  Use the **Improve with AI** button to automatically optimize your prompt.  
+  This provides AI-generated suggestions to enhance clarity, tone, and structure — helping improve prompt performance and consistency.
+
+---
+
+## Using Prompts in Pipelines
+
+Once registered, prompts can be used in downstream applications:
+
+```python
+# Reference the prompt in pipeline code
+intent_result = customer_intent_classification_prompt(
+    user_message=user_input
+)
+
+# Access the classified intent
+classified_intent = intent_result["classified_intent"]
+
+# Use in downstream logic
+if classified_intent == "ACTIVATE CARD":
+    # Handle card activation
+    pass
+elif classified_intent == "BLOCK CARD":
+    # Handle card blocking
+    pass
+```
+
+---
+
+## Next Steps
+
+After registering your prompt:
+
+1. **Register a model** - If you haven't already, register the LLM to use with this prompt
+2. **Build a pipeline** - Combine your prompt with a model and other resources to create a use-case specific pipeline. 
+
+---
+
+
+## Related Documentation
+
+- [Model Registration Guide](../model/) - Register LLM models to use with prompts
+
+---