LLMs, GenAI, RAG, agents, evaluation · insight-0562

Check everything verifiable with code before reaching for an LLM judge

Situation
An eval pipeline sends every output to an LLM judge, including checks such as valid JSON, word limits and whether the booking reference matches the input.
Action
Split criteria into verifiable (schema validity, exact match, numeric tolerance, length, required keywords, unit tests) and judgment-based; implement the first group in code and reserve the judge for the rest.
Reason
Instruction-following benchmarks built on automatically verifiable instructions avoid both human cost and judge bias for those criteria.
Boundary
Any LLM eval suite.

Symptom or risk

Objective failures are scored inconsistently, judge costs grow, and judge bias contaminates criteria that never needed judgment.

Criteria such as tone, helpfulness and faithfulness of paraphrase still need human or model judgment.

Writing checks takes engineering time, repaid on every run.

Asking a judge whether a number is correct when the correct number is known.

Zhou et al. built IFEval around automatically verifiable instructions, such as word counts and keyword frequency, to avoid the cost of human evaluation and the bias of LLM judges.
Instruction-Following Evaluation for Large Language Models