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When you need to add new input variables to your prompt template, it’s important to keep your source code in sync with the template changes. This guide outlines the process for deploying these updates to your production environment without any downtime.

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Input Variable Handling

The pl.run() function handles input variables in the following ways:

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1. Normal Usage

Provide all required variables as defined in your prompt template: 
response = pl.run(
   prompt_name="movie_recommender",
   prompt_release_label="prod",
   input_variables={
       "favorite_movie": "The Shawshank Redemption"
   },
)

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2. Missing Variables

If you don’t provide the required input variables, you’ll receive a warning in the console, but the prompt template will still run. The missing variables will be sent to the LLM as unprocessed strings: 
response = pl.run(
   prompt_name="movie_recommender",
   prompt_release_label="prod",
   input_variables={},
)

WARNING: While getting your prompt template: Some input variables are missing: (`favorite_movie`)
Undefined variable in message index 1: 'favorite_movie' is undefined

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3. Extra Variables

If you include extra variables that aren’t in the template, they will be ignored: 
response = pl.run(
   prompt_name="movie_recommender",
   prompt_release_label="prod",
   input_variables={
       "favorite_movie": "The Shawshank Redemption",
       "release_year": 1994  # This will be ignored if not in template
   },
)
In this case, the release_year variable will be ignored in the LLM request if it’s not part of the current template.

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Zero Downtime Update Process

Follow these steps to safely add new variables to your prompt template without any service interruption:

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Example Scenario

Assume you have a prompt template version tagged with prod that uses only one input variable, favorite_movie: 
response = pl.run(
    prompt_name="movie_recommender",
    prompt_release_label="prod",
    input_variables={
        "favorite_movie": "The Shawshank Redemption"
    },
)
Now you want to add a new mood variable to enhance the recommendations.

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Step-by-Step Process

  1. Create a new template version with the new mood variable in the PromptLayer UI
  2. Apply a unique temporary label (e.g., new-var) to the new version in the UI
  3. Update and deploy your code to use the new template version and include the new variable: 
response = pl.run(
   prompt_name="movie_recommender",
   prompt_release_label="new-var",  # Temporary label
   input_variables={
       "favorite_movie": "The Shawshank Redemption",
       "mood": "uplifting"  # New variable
   },
)
  1. Verify the deployment is working correctly with the new variable
  2. In the PromptLayer UI, move the prod label to the new prompt version
  3. Update your source code to reference the prod prompt version again and deploy: 
response = pl.run(
   prompt_name="movie_recommender",
   prompt_release_label="prod",  # Back to prod
   input_variables={
       "favorite_movie": "The Shawshank Redemption",
       "mood": "uplifting"
   },
)
  1. Clean up by deleting the temporary new-var label from the PromptLayer UI

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Benefits of This Approach

  • Zero downtime: Your service remains available throughout the update process
  • Rollback capability: You can quickly revert to the previous version if issues arise
  • Gradual rollout: You can test the new version with a subset of traffic first
  • Version control: Both prompt templates and code changes are versioned and synchronized

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Best Practices

  1. Always test new variables in a staging environment first
  2. Use descriptive temporary labels that indicate the purpose (e.g., add-mood-var)
  3. Document variable changes in your commit messages and PR descriptions
  4. Consider using feature flags for more complex deployments
  5. Monitor logs during the deployment for any unexpected warnings

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Automation with CI/CD

You can automate this process using PromptLayer’s API in your CI/CD pipeline: 
# Example CI/CD script
import promptlayer

# Step 1: Create new version and apply temporary label
pl_client.create_prompt_template_version(...)
pl_client.apply_label("new-var", ...)

# Step 2: Deploy application code
# ... deployment logic ...

# Step 3: Move prod label to new version
pl_client.move_label("prod", to_version=new_version)

# Step 4: Clean up temporary label
pl_client.delete_label("new-var")
This ensures consistent, repeatable deployments without manual intervention.