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docs/concepts/managed-llms/managed-language-models.md

@@ -39,4 +39,4 @@ Assume you have a web service like the following, which uses the cloud native SD
 
 ## Deploying OpenAI-compatible apps
 
-If you already have an OpenAI-compatible application, Defang makes it easy to deploy on your favourite cloud's managed LLM service. See our [OpenAI Access Gateway](/docs/concepts/managed-llms/openai-access-gateway)
+If you already have an OpenAI-compatible application, Defang makes it easy to deploy on your favourite cloud's managed LLM service. See our [OpenAI Access Gateway](/docs/concepts/managed-llms/openai-access-gateway).

docs/concepts/managed-llms/openai-access-gateway.md

@@ -9,7 +9,7 @@ sidebar_position: 3000
 Defang makes it easy to deploy on your favourite cloud's managed LLM service with our [OpenAI Access Gateway](https://github.com/DefangLabs/openai-access-gateway). This service sits between your application and the cloud service and acts as a compatibility layer.
 It handles incoming OpenAI requests, translates those requests to the appropriate cloud-native API, handles the native response, and re-constructs an OpenAI-compatible response.
 
-See [our tutorial](/docs/tutorials/deploy-openai-apps) which describes how to configure the OpenAI Access Gateway for your application
+See [our tutorial](/docs/tutorials/deploy-openai-apps) which describes how to configure the OpenAI Access Gateway for your application.
 
 ## Docker Provider Services
 
@@ -37,7 +37,7 @@ The `x-defang-llm` extension is used to configure the appropriate roles and perm
 
 ## Model Mapping
 
-Defang supports model mapping through the [openai-access-gateway](https://github.com/DefangLabs/openai-access-gateway) on AWS and GCP. This takes a model with a Docker naming convention (e.g. ai/lama3.3) and maps it to the closest matching model name on the target platform. If no such match can be found it can fallback onto a known existing model (e.g. ai/mistral). These environment variables are USE_MODEL_MAPPING (default to true) and FALLBACK_MODEL (no default), respectively.
+Defang supports model mapping through the [openai-access-gateway](https://github.com/DefangLabs/openai-access-gateway) on AWS and GCP. This takes a model with a Docker naming convention (e.g. `ai/llama3.3`) and maps it to the closest matching model name on the target platform. If no such match can be found it can fallback onto a known existing model (e.g. `ai/mistral`). These environment variables are `USE_MODEL_MAPPING` (default to true) and `FALLBACK_MODEL` (no default), respectively.
 
 ## Current Support
 

docs/tutorials/deploy-openai-apps-aws-bedrock.mdx

@@ -1,9 +1,9 @@
 ---
-title: Deploying your OpenAI Application to AWS Bedrock
+title: Deploy OpenAI Apps to AWS Bedrock
 sidebar_position: 50
 ---
 
-# Deploying Your OpenAI Application to AWS Bedrock
+# Deploy OpenAI Apps to AWS Bedrock
 
 Let's assume you have an app that uses an OpenAI client library and you want to deploy it to the cloud on **AWS Bedrock**.  
 
@@ -50,7 +50,7 @@ Add **Defang's [openai-access-gateway](https://github.com/DefangLabs/openai-acce
 - The container image is based on [aws-samples/bedrock-access-gateway](https://github.com/aws-samples/bedrock-access-gateway), with enhancements.
 - `x-defang-llm: true` signals to **Defang** that this service should be configured to use target platform AI services.
 - New environment variables:
-  - `REGION` is the zone where the services runs (for AWS this is the equvilent of AWS_REGION)
+  - `REGION` is the zone where the services runs (for AWS, this is the equivalent of AWS_REGION)
 
 :::tip
 **OpenAI Key**
@@ -110,9 +110,9 @@ Choose the correct `MODEL` depending on which cloud provider you are using.
 - For **AWS Bedrock**, use a Bedrock model ID (e.g., `anthropic.claude-3-sonnet-20240229-v1:0`) [See available Bedrock models](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html).
 :::
 
-Alternatively, Defang supports model mapping through the openai-access-gateway. This takes a model with a Docker naming convention (e.g. `ai/lama3.3`) and maps it to
-the closest equilavent on the target platform. If no such match can be found a fallback can be defined to use a known existing model (e.g. ai/mistral). These environment
-variables are USE_MODEL_MAPPING (default to true) and FALLBACK_MODEL (no default), respectively.
+Alternatively, Defang supports model mapping through the [openai-access-gateway](https://github.com/DefangLabs/openai-access-gateway). This takes a model with a Docker naming convention (e.g. `ai/llama3.3`) and maps it to
+the closest equivalent on the target platform. If no such match can be found ,a fallback can be defined to use a known existing model (e.g. `ai/mistral`). These environment
+variables are `USE_MODEL_MAPPING` (default to true) and `FALLBACK_MODEL` (no default), respectively.
 
 
 :::info
@@ -151,7 +151,7 @@ services:
 | Variable           | AWS Bedrock |
 |--------------------|-------------|
 | `REGION`            | Required|
-| `MODEL`             | Bedrock model ID or Docker model name, for example `meta.llama3-3-70b-instruct-v1:0` or `ai/lama3.3` |
+| `MODEL`             | Bedrock model ID or Docker model name, for example `meta.llama3-3-70b-instruct-v1:0` or `ai/llama3.3` |
 
 ---
 

docs/tutorials/deploy-openai-apps-gcp-vertex.mdx

@@ -28,7 +28,7 @@ services:
 
 ## Add an LLM Service to Your Compose File
 
-You need to add a new service that acts as a proxy between your app and the backend LLM provider (Vertex).
+You need to add a new service that acts as a proxy between your app and the backend LLM provider (Vertex AI).
 
 Add **Defang's [openai-access-gateway](https://github.com/DefangLabs/openai-access-gateway)** service:
 
@@ -112,11 +112,11 @@ To do this, you can check your [AWS Bedrock model access](https://docs.aws.amazo
 :::info
 **Choosing the Right Model**
 
-- For **GCP Vertex AI**, use a full model path (e.g., `google/gemini-2.5-pro-preview-03-25`) [See available Vertex models](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/call-vertex-using-openai-library#client-setup)
+- For **GCP Vertex AI**, use a full model path (e.g., `google/gemini-2.5-pro-preview-03-25`). [See available Vertex AI models](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/call-vertex-using-openai-library#client-setup).
 :::
 
-Alternatively, Defang supports model mapping through the openai-access-gateway. This takes a model with a Docker naming convention (e.g. ai/lama3.3) and maps it to
-the closest matching one on the target platform. If no such match can be found it can fallback onto a known existing model (e.g. ai/mistral). These environment
+Alternatively, Defang supports model mapping through the [openai-access-gateway](https://github.com/DefangLabs/openai-access-gateway). This takes a model with a Docker naming convention (e.g. `ai/llama3.3`) and maps it to
+the closest matching one on the target platform. If no such match can be found, it can fallback onto a known existing model (e.g. `ai/mistral`). These environment
 variables are `USE_MODEL_MAPPING` (default to true) and `FALLBACK_MODEL` (no default), respectively.