When working with Retrieval-Augmented Generation (RAG), visibility into each step of the pipeline is essential. That’s where LLM observability tools like Arize Phoenix come in.

🔍 What can Arize do?

• Trace prompts through your entire RAG pipeline
• Capture both system-wide and component-level metrics
• Log traffic and experiment with new system configurations

📊 Why it’s valuable:

• See how prompt changes affect final performance
• A/B test system tweaks and monitor impact
• Build reports to track system evolution

🔄 It’s a flywheel: Better observability → Better experiments → Better performance

🔧 Of course, no single tool solves everything. Tools like Datadog and Grafana remain crucial for full-stack observability.

💡 Tip: Iteratively try prompts, run experiments, and refine your system based on real-world usage.

Let’s build smarter, more transparent AI systems.
🧠 Created by Rachel Li | rachellearns.com

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