TraceroAI
Debug RAG failures before they reach users.
TraceroAI helps AI teams trace, evaluate, and understand why retrieval-augmented generation systems produce bad answers.
Primary use case
RAG debugging
Core signal
Groundedness failures
Status
In active development
Product
A debugger for the full RAG answer lifecycle.
Trace every RAG answer
Capture the user question, retrieval step, selected context, prompt, model response, latency, and cost in one timeline.
Inspect retrieved evidence
See which chunks were used, how relevant they were, and whether the answer was actually supported by the retrieved context.
Find hallucination causes
Separate retrieval failures from unsupported generation, noisy context, stale documents, and prompt-level issues.
Validate improvements
Compare fixes across evaluation cases so better prompts, retrievers, and model settings can be tested before release.
Diagnosis
Bad answers are symptoms. TraceroAI shows the cause.
A hallucinated answer is not always a model problem. Sometimes the retriever missed the right document. Sometimes the context was noisy. Sometimes the prompt let the model over-answer. TraceroAI is built to separate these failure modes.
Unsupported claim
Retrieval miss
Noisy context
Stale source
Weak answer relevance
Latency spike
Case Study
Built as an end-to-end AI engineering project.
TraceroAI is being developed to demonstrate production-level AI product engineering: tracing, evaluation, reliability workflows, backend systems, and a focused developer experience for teams working with RAG applications.
Current build focus
Shipping the first working debugger flow: trace a RAG response, inspect retrieved chunks, evaluate groundedness, and explain the failure.