G-Eval lets LLMs self‑grade text with stable, rubric‑driven scores
The blog breaks down the G‑Eval framework, where a LLM generates its own evaluation steps from a detailed rubric and computes a weighted expectation over token probabilities instead of reading a printed digit. This yields more consistent, reference‑free scores for open‑ended outputs like summaries or chatbot replies, making automated quality checks trustworthy.