diff --git a/docs/my-website/docs/completion/json_mode.md b/docs/my-website/docs/completion/json_mode.md
index 0e7e64a8ec..92e135dff5 100644
--- a/docs/my-website/docs/completion/json_mode.md
+++ b/docs/my-website/docs/completion/json_mode.md
@@ -1,7 +1,7 @@
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 
-# JSON Mode
+# Structured Outputs (JSON Mode)
 
 ## Quick Start 
 
@@ -61,8 +61,180 @@ params = get_supported_openai_params(model="anthropic.claude-3", custom_llm_prov
 assert "response_format" in params
 ```
 
+## Pass in 'json_schema' 
+
+To use Structured Outputs, simply specify
+
+```
+response_format: { "type": "json_schema", "json_schema": … , "strict": true }
+```
+
+Works for OpenAI models 
+
+:::info
+
+Support for passing in a pydantic object to litellm sdk will be [coming soon](https://github.com/BerriAI/litellm/issues/5074#issuecomment-2272355842)
+
+:::
+
+<Tabs>
+<TabItem value="sdk" label="SDK">
+
+```python
+import os
+from litellm import completion 
+
+# add to env var 
+os.environ["OPENAI_API_KEY"] = ""
+
+messages = [{"role": "user", "content": "List 5 cookie recipes"}]
+
+resp = completion(
+    model="gpt-4o-2024-08-06",
+    messages=messages,
+    response_format={
+        "type": "json_schema",
+        "json_schema": {
+          "name": "math_reasoning",
+          "schema": {
+            "type": "object",
+            "properties": {
+              "steps": {
+                "type": "array",
+                "items": {
+                  "type": "object",
+                  "properties": {
+                    "explanation": { "type": "string" },
+                    "output": { "type": "string" }
+                  },
+                  "required": ["explanation", "output"],
+                  "additionalProperties": False
+                }
+              },
+              "final_answer": { "type": "string" }
+            },
+            "required": ["steps", "final_answer"],
+            "additionalProperties": False
+          },
+          "strict": True
+        },
+    }
+)
+
+print("Received={}".format(resp))
+```
+</TabItem>
+<TabItem value="proxy" label="PROXY">
+
+1. Add openai model to config.yaml
+
+```yaml
+model_list:
+  - model_name: "gpt-4o"
+    litellm_params:
+      model: "gpt-4o-2024-08-06"
+```
+
+2. Start proxy with config.yaml
+
+```bash
+litellm --config /path/to/config.yaml
+```
+
+3. Call with OpenAI SDK / Curl!
+
+Just replace the 'base_url' in the openai sdk, to call the proxy with 'json_schema' for openai models
+
+**OpenAI SDK**
+```python
+from pydantic import BaseModel
+from openai import OpenAI
+
+client = OpenAI(
+    api_key="anything", # 👈 PROXY KEY (can be anything, if master_key not set)
+    base_url="http://0.0.0.0:4000" # 👈 PROXY BASE URL
+)
+
+class Step(BaseModel):
+    explanation: str
+    output: str
+
+class MathReasoning(BaseModel):
+    steps: list[Step]
+    final_answer: str
+
+completion = client.beta.chat.completions.parse(
+    model="gpt-4o",
+    messages=[
+        {"role": "system", "content": "You are a helpful math tutor. Guide the user through the solution step by step."},
+        {"role": "user", "content": "how can I solve 8x + 7 = -23"}
+    ],
+    response_format=MathReasoning,
+)
+
+math_reasoning = completion.choices[0].message.parsed
+```
+
+**Curl**
+
+```bash
+curl -X POST 'http://0.0.0.0:4000/v1/chat/completions' \
+-H 'Content-Type: application/json' \
+-H 'Authorization: Bearer sk-1234' \
+-d '{
+    "model": "gpt-4o",
+    "messages": [
+      {
+        "role": "system",
+        "content": "You are a helpful math tutor. Guide the user through the solution step by step."
+      },
+      {
+        "role": "user",
+        "content": "how can I solve 8x + 7 = -23"
+      }
+    ],
+    "response_format": {
+      "type": "json_schema",
+      "json_schema": {
+        "name": "math_reasoning",
+        "schema": {
+          "type": "object",
+          "properties": {
+            "steps": {
+              "type": "array",
+              "items": {
+                "type": "object",
+                "properties": {
+                  "explanation": { "type": "string" },
+                  "output": { "type": "string" }
+                },
+                "required": ["explanation", "output"],
+                "additionalProperties": false
+              }
+            },
+            "final_answer": { "type": "string" }
+          },
+          "required": ["steps", "final_answer"],
+          "additionalProperties": false
+        },
+        "strict": true
+      }
+    }
+  }'
+```
+
+</TabItem>
+</Tabs>
+
+
 ## Validate JSON Schema 
 
+:::info
+
+Support for doing this in the openai 'json_schema' format will be [coming soon](https://github.com/BerriAI/litellm/issues/5074#issuecomment-2272355842)
+
+:::
+
 For VertexAI models, LiteLLM supports passing the `response_schema` and validating the JSON output.
 
 This works across Gemini (`vertex_ai_beta/`) + Anthropic (`vertex_ai/`) models.