I think many of us have felt the pain of building a cool LLM-powered application or RAG pipeline, only to find it's too brittle and unpredictable for real-world use. The core problem is that they are black boxes. When they fail, it's hard to know why.

I've been focused on this problem of "productionizing" AI workflows. It's not just about testing; it's about deep observability, performance tuning, and building systems you can trust to be stable.

I wrote up a guide on a methodology I've found very effective. It's based on an open-source framework that uses decorators to trace the entire execution path of a chatbot. This gives you the data to:

- Pinpoint Performance Bottlenecks: See the exact latency of every LLM call, tool use, and retrieval step. - Automate Quality Control: Use an LLM-as-a-judge to programmatically check for hallucinations (groundedness), safety violations, and adherence to custom rules. - Create a Feedback Loop for Improvement: When you change a prompt or logic, you can run the test suite and get a concrete report on whether performance and reliability have improved or worsened.

You can read the guide here: - LangChain-based application: https://alexostrovskyy.com/the-glass-box-why-your-chatbot-ne..., - LlamaIndex-based application: https://alexostrovskyy.com/production-llm-chatbot-tracing-an...

I’ve created this open-source project to use in my projects and help other creators.

My goal is to create a framework (open-source) that can help us build stable, trustworthy AI systems, not just clever demos.

I'd be very interested to hear feedback from other engineers and creators.