Over the last few months, I've spent a lot of time building RAG applications and experimenting with different retrieval strategies, prompts, and models.
One thing I noticed quickly was that when an answer was wrong, it was difficult to understand what actually failed.
Was the wrong document retrieved?
Was the context insufficient?
Did the model ignore the context and generate something unsupported?
Most tools could tell me that an answer was bad, but very few could explain why.
That led me to build TraceroAI, an open-source platform for evaluating and debugging RAG applications.
The platform captures the full lifecycle of an AI response, including the query, retrieved context, prompt, generated answer, latency, and token usage. It then evaluates each trace and identifies where failures occur.
Some of the features include:
- Python SDK published on PyPI
- RAG evaluation workflows
- LLM-as-Judge groundedness analysis
- Cost, token, and latency tracking
- Recovery workflows powered by LangGraph
- Dashboard for inspecting traces and failures
The biggest lesson from building TraceroAI is that improving AI systems is not just about better models. It's about having the right feedback loops.
When developers can clearly see why a response failed, they can iterate faster, make better decisions, and build more reliable products.
Building this project also gave me a deeper appreciation for AI evaluation, observability, and developer tooling. As AI applications move into production, these areas will become increasingly important.
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