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skills/json-outputs-implementer/workflow/phase-5-production.md

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+# Phase 5: Production Optimization
+
+**Objective**: Optimize for performance, cost, and reliability
+
+## 1. Grammar Caching Strategy
+
+The first request compiles a grammar from your schema (~extra latency). Subsequent requests use cached grammar (24-hour TTL).
+
+**Cache Invalidation Triggers:**
+- Schema structure changes
+- Tool set changes (if using tools + JSON outputs together)
+- 24 hours of non-use
+
+**Best Practices:**
+```python
+# ✅ Good: Finalize schema before production
+CONTACT_SCHEMA = ContactInfo  # Reuse same schema
+
+# ❌ Bad: Dynamic schema generation
+def get_schema(include_phone: bool):  # Different schemas = cache misses
+    if include_phone:
+        class Contact(BaseModel):
+            phone: str
+            ...
+    ...
+```
+
+## 2. Token Cost Management
+
+Structured outputs add tokens via system prompt:
+```python
+# Monitor token usage
+response = client.beta.messages.parse(...)
+print(f"Input tokens: {response.usage.input_tokens}")
+print(f"Output tokens: {response.usage.output_tokens}")
+
+# Optimize descriptions for token efficiency
+# ✅ Good: Concise but clear
+name: str = Field(description="Full name")
+
+# ❌ Excessive: Too verbose
+name: str = Field(description="The complete full name of the person including first name, middle name if available, and last name")
+```
+
+## 3. Monitoring
+
+```python
+import time
+from dataclasses import dataclass
+
+@dataclass
+class StructuredOutputMetrics:
+    latency_ms: float
+    input_tokens: int
+    output_tokens: int
+    cache_hit: bool  # Infer from latency
+    stop_reason: str
+
+def track_metrics(response, start_time) -> StructuredOutputMetrics:
+    latency = (time.time() - start_time) * 1000
+
+    return StructuredOutputMetrics(
+        latency_ms=latency,
+        input_tokens=response.usage.input_tokens,
+        output_tokens=response.usage.output_tokens,
+        cache_hit=latency < 500,  # Heuristic: fast = cache hit
+        stop_reason=response.stop_reason,
+    )
+
+# Track in production
+metrics = track_metrics(response, start_time)
+if metrics.latency_ms > 1000:
+    logger.warning(f"Slow structured output: {metrics.latency_ms}ms")
+```
+
+## Output
+
+Production-optimized implementation with caching and monitoring.