Removes references to Preconfigured LLM.

Author: szabosteve

deploy-manage/cloud-organization/billing/elastic-observability-billing-dimensions.md

@@ -22,11 +22,4 @@ Data volumes for ingest and retention are based on the fully enriched normalized
 
 [Synthetic monitoring](../../../solutions/observability/apps/synthetic-monitoring.md) is an optional add-on to Observability Serverless projects that allows you to periodically check the status of your services and applications. In addition to the core ingest and retention dimensions, there is a charge to execute synthetic monitors on our testing infrastructure. Browser (journey) based tests are charged per-test-run, and ping (lightweight) tests have an all-you-can-use model per location used.
 
-## Elastic Inference Service [EIS-billing]
-[Elastic Inference Service (EIS)](../../../explore-analyze/elastic-inference/eis.md) enables you to leverage AI-powered search as a service without deploying a model in your serverless project. EIS is configured as a default LLM for use with the Observability AI Assistant (for all observability projects).
-
-:::{note}
-Use of the Observability AI Assistant uses EIS tokens and incurs related token-based add-on billing for your serverless project.
-:::
-
 Refer to [Serverless billing dimensions](serverless-project-billing-dimensions.md) and the [{{ecloud}} pricing table](https://cloud.elastic.co/cloud-pricing-table?productType=serverless&project=observability) for more details about {{obs-serverless}} billing dimensions and rates.

solutions/observability/observability-ai-assistant.md

@@ -16,7 +16,7 @@ You can [interact with the AI Assistant](#obs-ai-interact) in two ways:
 * **Contextual insights**: Embedded assistance throughout Elastic UIs that explains errors and messages with suggested remediation steps.
 * **Chat interface**: A conversational experience where you can ask questions and receive answers about your data. The assistant uses function calling to request, analyze, and visualize information based on your needs.
 
-By default, AI Assistant uses a [preconfigured LLM](#preconfigured-llm-ai-assistant) connector that works out of the box. You can also connect to third-party LLM providers.
+The AI Assistant integrates with your large language model (LLM) provider through our supported {{stack}} connectors:
 
 ## Use cases
 
@@ -28,11 +28,6 @@ The {{obs-ai-assistant}} helps you:
 * **Build and execute queries**: Build Elasticsearch queries from natural language, convert Query DSL to ES|QL syntax, and execute queries directly from the chat interface 
 * **Visualize data**: Create time-series charts and distribution graphs from your Elasticsearch data
 
-## Preconfigured LLM [preconfigured-llm-ai-assistant]
-
-:::{include} ../_snippets/elastic-llm.md
-:::
-
 ## Requirements [obs-ai-requirements]
 
 The AI assistant requires the following:
@@ -45,7 +40,7 @@ The AI assistant requires the following:
 
     - To run {{obs-ai-assistant}} on a self-hosted Elastic stack, you need an [appropriate license](https://www.elastic.co/subscriptions).
 
-- If not using the [default preconfigured LLM](#preconfigured-llm-ai-assistant), you need an account with a third-party generative AI provider that preferably supports function calling. If your provider does not support function calling, you can configure AI Assistant settings under **Stack Management** to simulate function calling, but this might affect performance.
+- An account with a third-party generative AI provider that preferably supports function calling. If your AI provider does not support function calling, you can configure AI Assistant settings under **Stack Management** to simulate function calling, but this might affect performance.
 
   - The free tier offered by third-party generative AI provider may not be sufficient for the proper functioning of the AI assistant. In most cases, a paid subscription to one of the supported providers is required.
 
@@ -76,10 +71,6 @@ It's important to understand how your data is handled when using the AI Assistan
 
 ## Set up the AI Assistant [obs-ai-set-up]
 
-:::{note}
-If you use [the preconfigured LLM](#preconfigured-llm-ai-assistant) connector, you can skip this step. Your LLM connector is ready to use.
-:::
-
 The AI Assistant connects to one of these supported LLM providers:
 
 | Provider | Configuration | Authentication |

solutions/search/rag/playground.md

@@ -59,11 +59,6 @@ Here’s a simpified overview of how Playground works:
 
     * User can also **Download the code** to integrate into application
 
-## Elastic LLM [preconfigured-llm-playground]
-
-:::{include} ../../_snippets/elastic-llm.md
-:::
-
 ## Availability and prerequisites [playground-availability-prerequisites]
 
 For Elastic Cloud and self-managed deployments Playground is available in the **Search** space in {{kib}}, under **Content** > **Playground**.
@@ -77,7 +72,7 @@ To use Playground, you’ll need the following:
 
     * See [ingest data](playground.md#playground-getting-started-ingest) if you’d like to ingest sample data.
 
-3. If not using the default preconfigured LLM connector, you will need an account with a supported LLM provider:
+3. An account with a **supported LLM provider**. Playground supports the following:
 
     * **Amazon Bedrock**
 
@@ -119,11 +114,6 @@ You can also use locally hosted LLMs that are compatible with the OpenAI SDK. On
 
 ### Connect to LLM provider [playground-getting-started-connect]
 
-:::{note}
-If you use [the preconfigured LLM](#preconfigured-llm-playground) connector, you can skip this step. Your LLM connector is ready to use.
-
-:::
-
 To get started with Playground, you need to create a [connector](../../../deploy-manage/manage-connectors.md) for your LLM provider. You can also connect to [locally hosted LLMs](playground.md#playground-local-llms) which are compatible with the OpenAI API, by using the OpenAI connector.
 
 To connect to an LLM provider, follow these steps on the Playground landing page: