> ## Documentation Index
> Fetch the complete documentation index at: https://prompt-lockbox.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Fetch and Run

> Learn how pick up a prompt and test it quickly.

This guide shows the final and most important step: integrating a version-controlled prompt into your Python application using the SDK. While the `plb` command line is perfect for managing your library, the Python SDK is how you'll bring your prompts to life in a production environment.

**We will write a simple Python script that loads our joke-generator prompt, renders it with a specific topic, and prints the result**.

<Tip>
  **Best Practice: The "Latest Version" Advantage**

  A key feature of the SDK is that project.get\_prompt("name") automatically finds and uses the latest semantic version of that prompt. This means you can create v1.1.0 of joke-generator, and your application code will start using it immediately—no code changes required!
</Tip>

Here is a complete example of a Python script that uses the SDK to prepare the prompt and then gets a completion from an LLM.

```python theme={null}
from prompt_lockbox.api import Project
from rich import print # For pretty printing

# --- Part 1: Prepare the Prompt with Prompt Lockbox ---

# 1. Initialize your project.
try:
    project = Project()
except FileNotFoundError:
    print("Error: Not a PromptLockbox project. Have you run `plb init`?")
    exit()

# 2. Get a prompt by its name.
#    The SDK automatically finds the LATEST version.
prompt = project.get_prompt("joke-generator")

if not prompt:
    print("Error: Prompt 'joke-generator' not found!")
    exit()

# --- Part 2: Run the Prompt and Get a Response ---

# 3. The `run()` method handles everything:
#    - It renders the template with your variables.
#    - It calls the configured LLM API.
#    - It returns a structured response object.4

print("--- Calling the LLM via prompt.run()... ---")
try:
    result = prompt.run(topic="computers")
    
    # The result is a dictionary-like object with all the details
    print("\n--- ✅ Success! Here is the structured result: ---")
    rprint(result)

except Exception as e:
    print(f"Error during prompt execution: {e}")
```

Here's the terminal output:

```text theme={null}
--- Calling the LLM via prompt.run()... ---

--- ✅ Success! Here is the structured result: ---
{
    'prompt_name': 'joke-generator',
    'prompt_version': '1.1.0',
    'rendered_prompt': 'Tell me a dad joke about computers.',
    'llm_response': {
        'output_text': 'Why did the computer keep sneezing?\n\nIt had a virus!',
        'model_used': 'gpt-4o-mini',
        'usage': {
            'input_tokens': 15,
            'output_tokens': 12,
            'total_tokens': 27
        }
    }
}
```

### Next Steps

You've seen how to manage prompts on the command line and how the SDK provides a simple `run()` method to handle rendering and LLM execution.

<Check>
  **Where to Go Next?**

  Check out our [Use Cases](/usecase/first) section to learn how to:

  * Set up CI/CD pipelines to automatically lint and verify your prompts.
  * Manage complex team workflows with namespaces and status flags.
  * Leverage advanced search to build a truly discoverable prompt library.
</Check>