style: rename 'custom configuration' references to 'plugin'

doc/GOOD_CODING_PRACTICE.md

@@ -8,7 +8,7 @@ This project includes an extensive collection of Python unit tests for verifying
 
 ## Pydantic
 
-Originally created in 2017, [Pydantic](https://docs.pydantic.dev/latest/) has become the most widely used data validation library for Python. It is especially useful for data driven applications like this one, involving frequent integrations with a variety of cloud infrastructure services in a variety of environments, configured by a variety of different possible sources of data including environment variables, .env file, terraform.tfvars and system constants. We use it for the [Settings](../api/terraform/python/openai_api/common/conf.py) class in this project, and also for validating yaml [custom configurations](.api/terraform/python/openai_api/lambda_openai_function/custom_config.py) for the OpenAI Function Calling feature. It's an important addition because it enforces strong type and business rule validation checking of all of the configuration parameters for the AWS Lambdas, and it ensures that nothing ever changes these values at run-time once they've been set. And this in turn is important because erroneous automation code could otherwise lead to some wildly disastrous results. 😳
+Originally created in 2017, [Pydantic](https://docs.pydantic.dev/latest/) has become the most widely used data validation library for Python. It is especially useful for data driven applications like this one, involving frequent integrations with a variety of cloud infrastructure services in a variety of environments, configured by a variety of different possible sources of data including environment variables, .env file, terraform.tfvars and system constants. We use it for the [Settings](../api/terraform/python/openai_api/common/conf.py) class in this project, and also for validating yaml [plugins](.api/terraform/python/openai_api/lambda_openai_function/custom_config.py) for the OpenAI Function Calling feature. It's an important addition because it enforces strong type and business rule validation checking of all of the configuration parameters for the AWS Lambdas, and it ensures that nothing ever changes these values at run-time once they've been set. And this in turn is important because erroneous automation code could otherwise lead to some wildly disastrous results. 😳
 
 ## Automations