How this lab works
Build · RAG, in plain terms
This is where you prove the engine actually works. You give it a real document, ask real questions, and the evaluator scores every answer for trust — retrieval, faithfulness, citations, and hallucination risk — so 'it sounds right' is replaced with 'here's the evidence it's right.'
Load a document
Drop in a file or pick a sample. The lab splits it into passages (chunks) and indexes them for retrieval.
RAG answers from your content, not the model's memory — so the document is the source of truth.
Ask a question
Retrieval finds the most relevant passages, then the model drafts an answer with citations back to them.
You see the exact evidence behind every answer, not a black-box response.
Score every answer
The evaluator rates retrieval relevance, faithfulness, citation accuracy, and hallucination risk — and gives an honest verdict.
For anything customer-facing, 'sounds plausible' is not the same as 'grounded and correct.'
Inspect the trace
See which chunks were retrieved, which were actually used, and the step-by-step processing timeline.
When an answer is wrong, the trace shows you exactly where it broke — retrieval or generation.
Quality gate
Each answer passes, warns, or fails, and gets flagged for human review when the risk is too high.
An honest gate keeps untrustworthy answers out before they ever reach a user.
Compare runs
Stack model, retriever, and prompt versions side by side and catch regressions between them.
You can't improve what you don't measure across versions — this is how the engine gets better, safely.