Add introductory page in docs for monitoring

Author: SecroLoL

docs/monitoring/introduction.mdx

@@ -0,0 +1,30 @@
+---
+title: Peformance Monitoring Worfklows with Judgment
+---
+
+## Overview ##
+`judgeval` contains a suite of monitoring tools that allow you to **measure the quality of your LLM applications in production** scenarios. 
+
+Using `judgeval` in production, you can:
+- Measure the quality of your LLM agent systems in **real time** using Judgment's **10+ researched-backed scoring metrics**.
+  - Check for regressions in **retrieval quality, hallucinations, and any other scoring metric you care about**.
+- Measure token usage
+- Track latency of different system components (web searching, LLM generation, etc.)
+
+<Tip>
+    **Why evaluate your system in production?**
+    
+    Production data **provides the highest signal** for improving your LLM system on use cases you care about. 
+    Judgment Labs' infrastructure enables LLM teams to **capture quality signals from production use cases** and 
+    provides [**actionable insights**](/monitoring/production_insights) for improving any component of your system.
+</Tip>
+
+
+## Standard Setup ##
+A typical setup of `judgeval` on production systems involves:
+- Tracing your application using `judgeval`'s [tracing module](/monitoring/tracing).
+- Embedding evaluation runs into your traces the `async_evaluate()` function.
+- Tracking your LLM agent's performance in real time using the [Judgment platform](/judgment/introduction).
+
+
+For a full example of how to set up `judgeval` in a production system, see our [OpenAI Travel Agent example](/monitoring/tracing#example-openai-travel-agent).