LLM / RAG reliability & evaluation

I give you a number,
not a vibe.

Everyone ships an AI. Almost nobody can prove it is accurate. EvalGate is a harness that measures a RAG system's answer quality — accuracy, retrieval recall, groundedness — with a deterministic scorer (no LLM judge), catches hallucinations, and shows the before/after of a fix as a number.

deterministic scoring no LLM judge misses published reproducible run

END-TO-END ANSWER ACCURACY

Baseline

—

naive chunking + loose prompt

Hardened

—

section chunking + grounded prompt

Same corpus, same questions, same local model (temp 0). The only change is chunking + prompt — so the delta is attributable.

Synthetic demo. The corpus is a fictional product ("Nimbus"); every fact is invented for a self-contained, reproducible eval. What is real is the method: deterministic scoring you can audit line by line.

I give you a number,
not a vibe.

Scoreboard — Baseline vs Hardened

Spotlights — what the scorer caught

Per-question results

Regression — did the fix help, and did it break anything?

I give you a number,not a vibe.

Scoreboard — Baseline vs Hardened

Spotlights — what the scorer caught

Per-question results

Regression — did the fix help, and did it break anything?

I give you a number,
not a vibe.