wznhf78gyf-bot · Oct 7, 2026
diff --git a/aigw/api-reference/inference-api/config-object.mdx b/aigw/api-reference/inference-api/config-object.mdx
@@ -735,13 +735,13 @@ You can find more examples of schemas [below](/aigw/api-reference/inference-api/
 | Example | Description |
 |---------|-------------|
 | [Single Provider](#single-provider-with-provider-slug) | Basic setup with provider slug (recommended) |
-| [With Hyperparameters](#provider-slug-with-model--hyperparameters) | Override model and parameters |
+| [With Hyperparameters](#provider-slug-with-model-&-hyperparameters) | Override model and parameters |
 | [Cache + Retry](#provider-slug-with-cache-and-retry) | Enable caching and retry logic |
 | [Load Balancing](#load-balancing-with-provider-slugs) | Distribute traffic across providers |
 | [Fallback](#fallback-across-providers) | Automatic failover between providers |
 | [Combined Strategies](#load-balancing-and-fallback-combination) | Nested load balancing with fallback |
 | [Passthrough Target](#passthrough-target) | Defer provider resolution to the request |
-| [Legacy: API Key](#legacy-single-provider-with-api-key) | Direct API key usage |
+| [Legacy: API Key](#legacy-single-provider-with-virtual-key) | Direct API key usage |
 | [Legacy: Virtual Key](#legacy-single-provider-with-virtual-key) | Virtual key usage |
 
 ---

diff --git a/aigw/api-reference/inference-api/headers.mdx b/aigw/api-reference/inference-api/headers.mdx
@@ -20,7 +20,7 @@ Authenticate your requests with your AI Gateway API key, passed as `Bearer $PORT
 Environment variable: `PORTKEY_API_KEY`
 
 <Note>
-The `x-portkey-api-key` header remains active and continues to authenticate requests. `Authorization` is the standard way to authenticate into the gateway; use `x-portkey-api-key` when `Authorization` is already carrying provider credentials, as in [Provider Slug + Auth](#1-provider-slug-auth) below.
+The `x-portkey-api-key` header remains active and continues to authenticate requests. `Authorization` is the standard way to authenticate into the gateway; use `x-portkey-api-key` when `Authorization` is already carrying provider credentials, as in [Provider Slug + Auth](#1-provider-slug-+-auth) below.
 </Note>
 <Accordion title="Example">
 <CodeGroup>

diff --git a/aigw/help-center/mcp-gateway-troubleshooting.mdx b/aigw/help-center/mcp-gateway-troubleshooting.mdx
@@ -35,7 +35,7 @@ MCP Gateway authenticates in two places: your client → Prisma AIRS AI Gateway
 | `401` | Missing/expired token, or the MCP server needs you to sign in | Re-authenticate; if prompted, complete the server's sign-in |
 | `403` | You're authenticated but not allowed | Wrong workspace/server in the URL, or you don't have access to that server |
 | `404` | Server not found | The slug in your URL is wrong, or the server isn't registered in this workspace |
-| `500` "Unable to verify user access" | The gateway couldn't confirm your access | See [API key type](#can-t-connect-or-access-denied) below |
+| `500` "Unable to verify user access" | The gateway couldn't confirm your access | See [API key type](#can’t-connect-or-access-denied) below |
 
 ---
 

diff --git a/aigw/integrations/llms/azure-openai/batches.mdx b/aigw/integrations/llms/azure-openai/batches.mdx
@@ -12,7 +12,7 @@ This is the most efficient way to
 <Info>
 The AI Gateway supports **two modes** on Azure OpenAI:
 * **[Provider Batch API](#using-azure-openai-batch-api-through-the-ai-gateway)** (cheapest, completion_window: `24h`, `48h`, etc.)
-* **[AI Gateway Batch API](/aigw/product/ai-gateway/batches#portkey-batch-api-mode)** (fast, provider-agnostic, completion_window: `immediate`)
+* **[AI Gateway Batch API](/aigw/product/ai-gateway/batches#provider-batch-api-mode)** (fast, provider-agnostic, completion_window: `immediate`)
 </Info>
 
 ## Using Azure OpenAI Batch API through the AI Gateway

diff --git a/aigw/integrations/llms/bedrock/aws-bedrock.mdx b/aigw/integrations/llms/bedrock/aws-bedrock.mdx
@@ -57,7 +57,7 @@ If you're using [AWS Security Token Service](https://docs.aws.amazon.com/STS/lat
 
 #### Not using Bedrock Provider from Model Catalog?
 
-[Check out this example on how you can directly use your AWS details to make a Bedrock request through the AI Gateway.](/aigw/integrations/llms/bedrock/aws-bedrock#making-requests-without-using-portkey%E2%80%99s-model-catalog)
+[Check out this example on how you can directly use your AWS details to make a Bedrock request through the AI Gateway.](/aigw/integrations/llms/bedrock/aws-bedrock#making-requests-without-using-the-ai-gateway’s-model-catalog)
 
 ### **3\. Invoke Chat Completions with AWS bedrock**
 

diff --git a/aigw/integrations/llms/bedrock/batches.mdx b/aigw/integrations/llms/bedrock/batches.mdx
@@ -17,7 +17,7 @@ This is the most efficient way to
 <Info>
 The AI Gateway supports **two modes** on Bedrock:
 * **[Provider Batch API](#using-bedrock-batch-api-through-the-ai-gateway)** (cheapest, completion_window: `24h`, `48h`, etc.)
-* **[AI Gateway Batch API  ⭐️](/aigw/product/ai-gateway/batches#portkey-batch-api-mode)** (fast, provider-agnostic, completion_window: `immediate`)
+* **[AI Gateway Batch API  ⭐️](/aigw/product/ai-gateway/batches#provider-batch-api-mode)** (fast, provider-agnostic, completion_window: `immediate`)
 </Info>
 
 ## Before You Start

diff --git a/aigw/integrations/llms/vertex-ai/batches.mdx b/aigw/integrations/llms/vertex-ai/batches.mdx
@@ -17,7 +17,7 @@ With Prisma AIRS AI Gateway, you can perform batch inference operations with Ver
 <Info>
 The AI Gateway supports **two modes** on Vertex:
 * **[Provider Batch API](#using-vertex-batch-api-through-the-ai-gateway)** (cheapest, completion_window: `24h`, `48h`, etc.)
-* **[AI Gateway Batch API](/aigw/product/ai-gateway/batches#portkey-batch-api-mode)** (fast, provider-agnostic, completion_window: `immediate`)
+* **[AI Gateway Batch API](/aigw/product/ai-gateway/batches#provider-batch-api-mode)** (fast, provider-agnostic, completion_window: `immediate`)
 </Info>
 
 ## Using Vertex Batch API through the AI Gateway

diff --git a/aigw/product/enterprise-offering/security.mdx b/aigw/product/enterprise-offering/security.mdx
@@ -26,7 +26,7 @@ These encryption standards are part of our commitment to maintaining secure data
 
 Our access control measures are designed with granularity to offer precise control over who can see and manage data.
 
-We provide [Role-Based Access Control (RBAC)](/aigw/product/enterprise-offering/access-control-management#id-2.-fine-grained-user-roles-and-permissions), allowing for stringent governance suited to complex organisational needs.
+We provide [Role-Based Access Control (RBAC)](/aigw/product/enterprise-offering/access-control-management#2-fine-grained-user-roles-and-permissions), allowing for stringent governance suited to complex organisational needs.
 
 This system is pivotal for enforcing security policies and ensuring that only authorized personnel have access to sensitive operations and data.
 

diff --git a/api-reference/inference-api/config-object.mdx b/api-reference/inference-api/config-object.mdx
@@ -737,13 +737,13 @@ You can find more examples of schemas [below](/api-reference/inference-api/confi
 | Example | Description |
 |---------|-------------|
 | [Single Provider](#single-provider-with-provider-slug) | Basic setup with provider slug (recommended) |
-| [With Hyperparameters](#provider-slug-with-model--hyperparameters) | Override model and parameters |
+| [With Hyperparameters](#provider-slug-with-model-&-hyperparameters) | Override model and parameters |
 | [Cache + Retry](#provider-slug-with-cache-and-retry) | Enable caching and retry logic |
 | [Load Balancing](#load-balancing-with-provider-slugs) | Distribute traffic across providers |
 | [Fallback](#fallback-across-providers) | Automatic failover between providers |
 | [Combined Strategies](#load-balancing-and-fallback-combination) | Nested load balancing with fallback |
 | [Passthrough Target](#passthrough-target) | Defer provider resolution to the request |
-| [Legacy: API Key](#legacy-single-provider-with-api-key) | Direct API key usage |
+| [Legacy: API Key](#legacy-single-provider-with-virtual-key) | Direct API key usage |
 | [Legacy: Virtual Key](#legacy-single-provider-with-virtual-key) | Virtual key usage |
 
 ---

diff --git a/changelog/enterprise.mdx b/changelog/enterprise.mdx
@@ -1279,7 +1279,7 @@ New `x-portkey-sensitive-headers` header (SDK aliases: `sensitive_headers` / `se
 
 `reasoning_effort` (`minimal` / `low` / `medium` / `high` / `none`) now works for Bedrock Claude reasoning models — mapped to adaptive thinking on Claude 4.6, and to `thinking.budget_tokens` on earlier reasoning models.
 
-[Bedrock `reasoning_effort` Documentation](/integrations/llms/bedrock/aws-bedrock#using-reasoning-effort-parameter)
+[Bedrock `reasoning_effort` Documentation](/integrations/llms/bedrock/aws-bedrock#using-reasoning_effort-parameter)
 
 ### Agent Gateway (Preview)
 
@@ -1706,7 +1706,7 @@ Added support for Databricks Model Serving as a new provider with support for ch
 
 Together AI now supports reasoning/thinking models with `reasoning_effort` parameter and `content_blocks` in the response. When `strictOpenAiCompliance` is set to `false`, the response includes structured `content_blocks` with both thinking content and the final text response. Both streaming and non-streaming modes are supported.
 
-[Together AI Documentation](/integrations/llms/together-ai#reasoning--thinking-support)
+[Together AI Documentation](/integrations/llms/together-ai#reasoning-/-thinking-support)
 
 ### Fixes and Improvements
 - **Unified Messages & Responses API**: Fixed issues when a request config had a combination of native and non-native providers (e.g., load balancing across Anthropic and OpenAI). The adapter decision is now made per-provider, ensuring correct behavior for mixed configs.
@@ -3158,7 +3158,7 @@ We are redacting this release and will be releasing a patch with out Workspace B
 - Updated the unified API signature for Extended thinking which was introduced in v1.10.12 to ensure that OpenAI compliant field of the response remain untouched regardless of strict_open_ai_compliance flag.
 - More Details:
     - [Anthropic](/integrations/llms/anthropic#extended-thinking-reasoning-models)
-    - [AWS Bedrock](/integrations/llms/bedrock/aws-bedrock#extended-thinking-reasoning-models)
+    - [AWS Bedrock](/integrations/llms/bedrock/aws-bedrock#extended-thinking-reasoning-models-beta)
     - [VertexAI](/integrations/llms/vertex-ai#extended-thinking-reasoning-models)
 
 ### Unified Batches API Improvements
@@ -3210,7 +3210,7 @@ We are redacting this release and will be releasing a patch with out Workspace B
 - Introduced a unified API signature to support single-turn and multi-turn conversations with Anthropic Extended Reasoning across Anthropic, AWS Bedrock and VertexAI.
 - More Details:
     - [Anthropic](/integrations/llms/anthropic#extended-thinking-reasoning-models)
-    - [AWS Bedrock](/integrations/llms/bedrock/aws-bedrock#extended-thinking-reasoning-models)
+    - [AWS Bedrock](/integrations/llms/bedrock/aws-bedrock#extended-thinking-reasoning-models-beta)
     - [VertexAI](/integrations/llms/vertex-ai#extended-thinking-reasoning-models)
 
 ### Prometheus Metric Updates

diff --git a/guides/use-cases/few-shot-prompting.mdx b/guides/use-cases/few-shot-prompting.mdx
@@ -65,7 +65,7 @@ In this configuration, `{{few_shot_examples}}` is a placeholder for the few-shot
     <img src="/images/guides/use-case-1.png"/>
 </Frame>
 
-1. Click on it and paste the [raw prompt code from above](/guides/use-cases/few-shot-prompting#this-is-how-our-raw-prompt-would-look). And that's it! You have your **dynamically updatable** few shot prompt template ready to deploy.
+1. Click on it and paste the [raw prompt code from above](/guides/use-cases/few-shot-prompting#this-is-how-our-raw-prompt-looks). And that's it! You have your **dynamically updatable** few shot prompt template ready to deploy.
 
 ## Deploying the Prompt with Portkey
 

diff --git a/help-center/mcp-gateway-troubleshooting.mdx b/help-center/mcp-gateway-troubleshooting.mdx
@@ -35,7 +35,7 @@ MCP Gateway authenticates in two places: your client → Portkey (using your API
 | `401` | Missing/expired token, or the MCP server needs you to sign in | Re-authenticate; if prompted, complete the server's sign-in |
 | `403` | You're authenticated but not allowed | Wrong workspace/server in the URL, or you don't have access to that server |
 | `404` | Server not found | The slug in your URL is wrong, or the server isn't registered in this workspace |
-| `500` "Unable to verify user access" | The gateway couldn't confirm your access | See [API key type](#can-t-connect-or-access-denied) below |
+| `500` "Unable to verify user access" | The gateway couldn't confirm your access | See [API key type](#can’t-connect-or-access-denied) below |
 
 ---
 

diff --git a/integrations/libraries/android-studio.mdx b/integrations/libraries/android-studio.mdx
@@ -13,7 +13,7 @@ description: 'Add observability, governance, and reliability to Android Studio w
 This guide shows how to add Portkey as a **Third-Party Remote Provider** in Android Studio in a few minutes.
 
 <Note>
-For enterprise deployments across teams, see [Enterprise Governance](#3-enterprise-governance).
+For enterprise deployments across teams, see [Enterprise Governance](#3-set-up-enterprise-governance).
 </Note>
 
 ## 1. Setup
@@ -80,7 +80,7 @@ After adding Portkey as a provider:
 The models shown are the ones you added in Portkey (e.g. `@openai-prod/gpt-4o`, `@anthropic-prod/claude-3-5-sonnet-20241022`). All traffic is routed through Portkey automatically.
 
 <Note>
-**Fallbacks, load balancing, or caching?** Create a [Portkey Config](/product/ai-gateway/configs), attach it to your API key, and use the config’s virtual model in Android Studio. See [Enterprise Governance](#3-enterprise-governance) for examples.
+**Fallbacks, load balancing, or caching?** Create a [Portkey Config](/product/ai-gateway/configs), attach it to your API key, and use the config’s virtual model in Android Studio. See [Enterprise Governance](#3-set-up-enterprise-governance) for examples.
 </Note>
 
 import AdvancedFeatures from '/snippets/portkey-advanced-features.mdx';

diff --git a/integrations/libraries/anythingllm.mdx b/integrations/libraries/anythingllm.mdx
@@ -13,7 +13,7 @@ AnythingLLM is an all-in-one Desktop & Docker AI application with built-in RAG a
 This guide shows how to configure AnythingLLM with Portkey in under 5 minutes.
 
 <Note>
-  For enterprise deployments across teams, see [Enterprise Governance](#3-enterprise-governance).
+  For enterprise deployments across teams, see [Enterprise Governance](#3-set-up-enterprise-governance).
 </Note>
 
 # 1. Setup
@@ -73,7 +73,7 @@ Change models by updating the **Chat Model** field:
 All requests route through Portkey automatically.
 
 <Note>
-**Want fallbacks, load balancing, or caching?** Create a [Portkey Config](/product/ai-gateway/configs), attach it to your API key, and set Chat Model to `dummy`. See [Enterprise Governance](#3-enterprise-governance) for examples.
+**Want fallbacks, load balancing, or caching?** Create a [Portkey Config](/product/ai-gateway/configs), attach it to your API key, and set Chat Model to `dummy`. See [Enterprise Governance](#3-set-up-enterprise-governance) for examples.
 </Note>
 
 import AdvancedFeatures from '/snippets/portkey-advanced-features.mdx';

diff --git a/integrations/libraries/cline.mdx b/integrations/libraries/cline.mdx
@@ -14,7 +14,7 @@ Cline is an AI coding assistant for VS Code. Add Portkey to get:
 This guide shows how to configure Cline with Portkey in under 5 minutes.
 
 <Note>
-  For enterprise deployments across teams, see [Enterprise Governance](#3-enterprise-governance).
+  For enterprise deployments across teams, see [Enterprise Governance](#3-set-up-enterprise-governance).
 </Note>
 
 # 1. Setup
@@ -82,7 +82,7 @@ Change models by updating the **Model ID** field:
 All requests route through Portkey automatically.
 
 <Note>
-**Want fallbacks, load balancing, or caching?** Create a [Portkey Config](/product/ai-gateway/configs), attach it to your API key, and set Model ID to `dummy`. See [Enterprise Governance](#3-enterprise-governance) for examples.
+**Want fallbacks, load balancing, or caching?** Create a [Portkey Config](/product/ai-gateway/configs), attach it to your API key, and set Model ID to `dummy`. See [Enterprise Governance](#3-set-up-enterprise-governance) for examples.
 </Note>
 
 import AdvancedFeatures from '/snippets/portkey-advanced-features.mdx';

diff --git a/integrations/libraries/codex.mdx b/integrations/libraries/codex.mdx
@@ -13,7 +13,7 @@ description: 'Add usage tracking, cost controls, and security guardrails to Code
 Configure Codex with Portkey in a few minutes.
 
 <Note>
-  For enterprise deployments across teams, see [Enterprise Governance](#3-enterprise-governance).
+  For enterprise deployments across teams, see [Enterprise Governance](#3-set-up-enterprise-governance).
 </Note>
 
 ## Quick setup with CLI
@@ -185,7 +185,7 @@ wire_api = "chat"
 
 
 <Note>
-**Fallbacks, load balancing, or caching?** Create a [Portkey Config](/product/ai-gateway/configs), attach it to your API key, and set `model` to the config’s virtual model. See [Enterprise Governance](#3-enterprise-governance) for examples.
+**Fallbacks, load balancing, or caching?** Create a [Portkey Config](/product/ai-gateway/configs), attach it to your API key, and set `model` to the config’s virtual model. See [Enterprise Governance](#3-set-up-enterprise-governance) for examples.
 </Note>
 
 import AdvancedFeatures from '/snippets/portkey-advanced-features.mdx';

diff --git a/integrations/libraries/conductor.mdx b/integrations/libraries/conductor.mdx
@@ -11,7 +11,7 @@ description: 'Add usage tracking, cost controls, and security guardrails to Cond
 - **Governance** — budget limits, usage tracking, and team access controls
 
 <Note>
-  For enterprise deployments across teams, see [Enterprise Governance](#3-enterprise-governance).
+  For enterprise deployments across teams, see [Enterprise Governance](#3-set-up-enterprise-governance).
 </Note>
 
 ## 1. Setup

diff --git a/integrations/libraries/continue-dev.mdx b/integrations/libraries/continue-dev.mdx
@@ -13,7 +13,7 @@ Continue.dev is an open-source AI coding assistant for VS Code and JetBrains. Ad
 This guide shows how to configure Continue.dev with Portkey in under 5 minutes.
 
 <Note>
-  For enterprise deployments across teams, see [Enterprise Governance](#3-enterprise-governance).
+  For enterprise deployments across teams, see [Enterprise Governance](#3-set-up-enterprise-governance).
 </Note>
 
 ---
@@ -258,7 +258,7 @@ models:
 ```
 
 <Note>
-**Want fallbacks, load balancing, or caching?** Create a [Portkey Config](/product/ai-gateway/configs), attach it to your API key, and set `x-portkey-config` to your Config ID in `requestOptions.headers`. See [Enterprise Governance](#3-enterprise-governance) for examples.
+**Want fallbacks, load balancing, or caching?** Create a [Portkey Config](/product/ai-gateway/configs), attach it to your API key, and set `x-portkey-config` to your Config ID in `requestOptions.headers`. See [Enterprise Governance](#3-set-up-enterprise-governance) for examples.
 </Note>
 
 ---

diff --git a/integrations/libraries/cursor.mdx b/integrations/libraries/cursor.mdx
@@ -15,7 +15,7 @@ description: "Add observability, governance, and reliability to Cursor with Port
 </Note>
 
 <Note>
-  For enterprise governance, see [Enterprise Governance](#enterprise-governance).
+  For enterprise governance, see [Enterprise Governance](#3-set-up-enterprise-governance).
 </Note>
 
 ## 1. Setup

diff --git a/integrations/libraries/goose.mdx b/integrations/libraries/goose.mdx
@@ -13,7 +13,7 @@ Goose is an open source AI agent that automates engineering tasks. Add Portkey t
 This guide shows how to configure Goose with Portkey in under 5 minutes.
 
 <Note>
-  For enterprise deployments across teams, see [Enterprise Governance](#3-enterprise-governance).
+  For enterprise deployments across teams, see [Enterprise Governance](#3-set-up-enterprise-governance).
 </Note>
 
 # 1. Setup
@@ -72,7 +72,7 @@ Change models by updating the model field:
 All requests route through Portkey automatically.
 
 <Note>
-**Want fallbacks, load balancing, or caching?** Create a [Portkey Config](/product/ai-gateway/configs), attach it to your API key, and set Model to `dummy`. See [Enterprise Governance](#3-enterprise-governance) for examples.
+**Want fallbacks, load balancing, or caching?** Create a [Portkey Config](/product/ai-gateway/configs), attach it to your API key, and set Model to `dummy`. See [Enterprise Governance](#3-set-up-enterprise-governance) for examples.
 </Note>
 
 import AdvancedFeatures from '/snippets/portkey-advanced-features.mdx';

diff --git a/integrations/libraries/janhq.mdx b/integrations/libraries/janhq.mdx
@@ -13,7 +13,7 @@ Jan is an open source alternative to ChatGPT that runs 100% offline on your comp
 This guide shows how to configure Jan with Portkey in under 5 minutes.
 
 <Note>
-  For enterprise deployments across teams, see [Enterprise Governance](#3-enterprise-governance).
+  For enterprise deployments across teams, see [Enterprise Governance](#3-set-up-enterprise-governance).
 </Note>
 
 # 1. Setup
@@ -69,7 +69,7 @@ Change models by updating the model selection:
 All requests route through Portkey automatically.
 
 <Note>
-**Want fallbacks, load balancing, or caching?** Create a [Portkey Config](/product/ai-gateway/configs), attach it to your API key, and set Model to `dummy`. See [Enterprise Governance](#3-enterprise-governance) for examples.
+**Want fallbacks, load balancing, or caching?** Create a [Portkey Config](/product/ai-gateway/configs), attach it to your API key, and set Model to `dummy`. See [Enterprise Governance](#3-set-up-enterprise-governance) for examples.
 </Note>
 
 import AdvancedFeatures from '/snippets/portkey-advanced-features.mdx';
-Original file line number
+Diff line change
@@ Expand Up @@
     </Note>
     <Note>
-      For enterprise governance, see [Enterprise Governance](#enterprise-governance).
+      For enterprise governance, see [Enterprise Governance](#3-set-up-enterprise-governance).
     </Note>
     ## 1. Setup
@@ Expand Down @@