Merge pull request #376 from DefangLabs/linda-llm-readme

Add playground to LLM readme

Author: Prakash-Sundaresan

samples/managed-llm-provider/README.md

@@ -14,6 +14,8 @@ You can configure the `MODEL` and `ENDPOINT_URL` for the LLM separately for loca
 
 Ensure you have enabled model access for the model you intend to use. To do this, you can check your [AWS Bedrock model access](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access-modify.html) or [GCP Vertex AI model access](https://cloud.google.com/vertex-ai/generative-ai/docs/control-model-access).
 
+For more about Managed LLMs in Defang, please see our [Managed LLMs documentation](https://docs.defang.io/docs/concepts/managed-llms/managed-language-models).
+
 ### Docker Model Provider
 
 In the `compose.yaml` file, the `llm` service will route requests to the LLM API model using a [Docker Model Provider](https://docs.defang.io/docs/concepts/managed-llms/openai-access-gateway#docker-model-provider-services).

samples/managed-llm/README.md

@@ -7,17 +7,21 @@ This sample application demonstrates the use of OpenAI-compatible Managed LLMs (
 
 > Note: Using Docker Model Provider? See our [*Managed LLM with Docker Model Provider*](https://github.com/DefangLabs/samples/tree/main/samples/managed-llm-provider) sample.
 
-The OpenAI-compatible managed LLM feature, provided by the Defang OpenAI Access Gateway, allows users to use AWS Bedrock or Google Cloud Vertex AI with an OpenAI compatible SDK. This enables switching from OpenAI to one of these cloud-native platforms without modifying your application code.
+Using the [Defang OpenAI Access Gateway](#defang-openai-access-gateway), the feature `x-defang-llm: true` enables you to use Managed LLMs on the Defang Playground or on platforms offered by BYOC providers (such as AWS Bedrock or GCP Vertex AI) with an OpenAI-compatible SDK.
+
+This allows switching from OpenAI to the Managed LLMs on supported cloud platforms without modifying your application code.
 
 You can configure the `MODEL` and `ENDPOINT_URL` for the LLM separately for local development and production environments.
 * The `MODEL` is the LLM Model ID you are using.
 * The `ENDPOINT_URL` is the bridge that provides authenticated access to the LLM model. 
 
 Ensure you have enabled model access for the model you intend to use. To do this, you can check your [AWS Bedrock model access](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access-modify.html) or [GCP Vertex AI model access](https://cloud.google.com/vertex-ai/generative-ai/docs/control-model-access).
 
+For more about Managed LLMs in Defang, please see our [Managed LLMs documentation](https://docs.defang.io/docs/concepts/managed-llms/managed-language-models).
+
 ### Defang OpenAI Access Gateway
 
-In the `compose.yaml` file, the `llm` service is used to route requests to the LLM API model. This is known as the Defang OpenAI Access Gateway. 
+In the `compose.yaml` file, the `llm` service is used to route requests to the LLM API model. This is known as the [Defang OpenAI Access Gateway](https://docs.defang.io/docs/concepts/managed-llms/openai-access-gateway). 
 
 The `x-defang-llm` property on the `llm` service must be set to `true` in order to use the OpenAI Access Gateway when deploying with Defang.