Evaluation Harness

Overview

Builds a robust, repeatable evaluation harness for LLMs. Covers golden dataset construction, golden answer types (exact, fuzzy, LLM-as-judge), metric selection (accuracy, BLEU/ROUGE/BERTScore, custom, faithfulness/relevance), eval runner script, LLM-as-judge prompt templates, regression tracking (diff between runs), and integration into CI or a dashboard.

When to Use This Skill

• Comparing multiple models or prompt versions on the same task.
• Establishing a quality baseline before shipping a feature that uses LLMs.
• Detecting quality regressions after model or prompt changes.
• The user wants "to measure how good this is" or "track quality over time".

Prerequisites

• A task with clear success criteria.
• A set of test cases (even 20-50 is valuable to start; aim for 100+ for statistical significance).
• Access to the models being evaluated.
• (Optional but recommended) A small amount of human-labeled "golden" answers.

Steps

1. Construct the eval dataset:

   • Representative of real usage (include edge cases and common failures).
   • Columns: id, input, expected_output (or rubric), metadata (difficulty, category).
   • Version the dataset (git + a simple JSONL or CSV).

2. Choose evaluation method per task:

   • Exact match / contains (for extraction, classification).
   • Fuzzy (Levenshtein, normalized edit distance).
   • BLEU / ROUGE / BERTScore (for generation, summarization).
   • LLM-as-judge (most flexible for open-ended tasks).
   • Human evaluation for high-stakes (sample).

3. LLM-as-judge (when used):

   • Write a strong judge prompt with clear rubric and few-shot examples.
   • Ask for a score (1-5 or 0-1) + reasoning.
   • Run the same judge on all candidates for fair comparison.
   • Validate the judge against human labels on a small set.

4. Build the eval runner:

   • Load dataset.
   • For each example: call model → parse output → compute metric(s).
   • Aggregate scores (mean, per-category breakdown, failure cases).
   • Save detailed results (JSON) + summary.

5. Regression tracking:

   • Store results with git commit / prompt version / model version.
   • Simple diff script: "compare run A vs run B — which examples got worse?"
   • Fail CI if score drops more than X% on key metrics.

6. Output:

   • Golden dataset template (with 10-20 examples).
   • evaluate.py script that is model- and prompt-agnostic.
   • LLM judge prompt template.
   • Results viewer (simple pandas + print or Streamlit).
   • CI integration example (GitHub Action step that runs eval and comments on PR).

Examples

A complete harness for "customer support ticket classification + draft response" task: golden dataset (JSONL), runner script, LLM-as-judge prompt for response quality, automatic metrics, and a regression diff report is included.

Edge Cases & Error Handling

• Judge bias: Use the same judge model/version for all comparisons. Calibrate against humans.
• Output parsing failures: Treat as score 0 and log the raw output.
• Cost of eval: Sample or use cheaper judge models for high-volume evals; run full eval nightly.

Verification

1. The harness runs end-to-end on the golden set without crashing.
2. Scores are reproducible when re-running the same model + prompt.
3. LLM judge scores correlate reasonably with human judgment on a validation subset (report the correlation).
4. A known-better prompt/model scores higher than a known-worse one.
5. Regression detection works (intentionally degrade a prompt and see the harness flag it).
6. Success: You have a trustworthy, automated way to know whether a change improved, hurt, or had no effect on quality.

References

• RAGAS
• ARES
• LLM-as-a-Judge
• HELM
• Big-bench