diff --git a/docs/v3/documentation/features/chat.mdx b/docs/v3/documentation/features/chat.mdx
index 83324b2e..6aab6996 100644
--- a/docs/v3/documentation/features/chat.mdx
+++ b/docs/v3/documentation/features/chat.mdx
@@ -94,6 +94,43 @@ for await (const chunk of responseStream.iter_text()) {
 
 Streaming is useful for displaying real-time responses in chat interfaces or when asking complex questions that require longer answers.
 
+## Structured Outputs
+
+When your application needs a machine-readable answer instead of prose, pass a schema as `response_format` and the answer is guaranteed to conform to it:
+
+<CodeGroup>
+```python Python
+from pydantic import BaseModel
+
+class OnboardingStatus(BaseModel):
+    completed: bool
+    remaining_steps: list[str]
+
+status = peer.chat(
+    "Has the user completed the onboarding flow?",
+    response_format=OnboardingStatus,
+)
+# status is a parsed OnboardingStatus instance
+```
+
+```typescript TypeScript
+import { z } from 'zod';
+
+const OnboardingStatus = z.object({
+  completed: z.boolean(),
+  remainingSteps: z.array(z.string()),
+});
+
+const status = await peer.chat(
+  "Has the user completed the onboarding flow?",
+  { responseFormat: OnboardingStatus },
+);
+// status is typed as z.infer<typeof OnboardingStatus>
+```
+</CodeGroup>
+
+The agent runs its full reasoning loop either way — only the final answer is formatted to your schema. See [Structured Outputs](/v3/documentation/features/advanced/structured-outputs) for the supported schema subset, streaming behavior, and best practices.
+
 ## Integration Patterns
 
 ### Dynamic Prompt Enhancement
@@ -189,48 +226,6 @@ const goals = await peer.chat("What are the user's main goals or objectives?");
 ```
 </CodeGroup>
 
-## Workspace-Level Chat
-
-While `peer.chat()` queries knowledge about a single peer, `honcho.chat()` searches across **all peers and observations** in the workspace. This is useful for cross-peer analysis, discovering common themes, or asking workspace-wide questions.
-
-<CodeGroup>
-```python Python
-from honcho import Honcho
-
-honcho = Honcho()
-
-# Ask about the entire workspace
-answer = honcho.chat("What are common themes across all users?")
-print(answer)
-
-# With streaming
-stream = honcho.chat_stream("Summarize all peer activity this week.")
-for chunk in stream:
-    print(chunk, end="", flush=True)
-
-# Async
-answer = await honcho.aio.chat("Which users have discussed topic X?")
-```
-
-```typescript TypeScript
-import { Honcho } from '@honcho-ai/sdk';
-
-const honcho = new Honcho({});
-
-// Ask about the entire workspace
-const answer = await honcho.chat("What are common themes across all users?");
-console.log(answer);
-
-// With streaming
-const stream = await honcho.chatStream("Summarize all peer activity this week.");
-for await (const chunk of stream) {
-  process.stdout.write(chunk);
-}
-```
-</CodeGroup>
-
-Workspace chat accepts `reasoning_level` and optional `session` scoping. For streaming, use the separate `chat_stream()` / `chatStream()` method rather than a `stream` parameter.
-
 ## How Honcho Answers
 
 When you call `peer.chat(query)`: