scaffolding-llm-eval-harness

A reproducible LLM evaluation harness prevents incomparable benchmark runs by enforcing versioned prompts, revision-pinned model IDs, and dataset hashing. It replaces ad hoc scripts with a structured scenario schema, ensuring run-to-run comparability.

You can compare candidate prompts and fine-tunes by running multi-scenario evaluations using a canonical scenario schema. The harness outputs JSONL result rows, enabling direct performance comparisons across different model revisions and prompt versions.

The best way to track prompt versioning and dataset hashing is to use a structured evaluation harness that stores canonical scenarios and pins model revisions. This logs JSONL result rows for consistent benchmark tracking.

Yes, you can use an optional judge model to grade LLM evaluation runs. The harness scaffolding supports integrating judge models alongside revision-pinned candidate models to automate scoring for complex scenarios like jailbreak testing or summarization.

Yes, this LLM evaluation harness works for RAG and jailbreak testing, along with summarization checks and fixed-test-set pipelines. It structures multi-scenario comparisons to validate guardrails and prompt effectiveness across these specific workflows.

You should not use a structured LLM evaluation harness for one-shot API calls or non-LLM classifier and retrieval evaluations. The scaffolding refuses to overbuild for single requests and redirects non-LLM tasks to more appropriate workflows.