I'm unhappy with Ragas scores that don't match human judgment

That’s a common and valid concern. Ragas scores are useful signals, not ground truth, and they often diverge from human judgment for a few reasons:

Why the mismatch happens

• Metric mismatch: Ragas may be scoring things like faithfulness, context recall, or answer relevance, while humans may care more about usefulness, completeness, tone, or task success.
• Weak evaluation context: If the retrieved context is incomplete, a perfectly good answer can score poorly.
• LLM-as-judge noise: Ragas often relies on LLM judgments, which can be inconsistent, overly sensitive to wording, or biased toward surface features.
• Domain specificity: In specialized domains, the evaluator model may not understand what “good” looks like.
• Prompting effects: Small changes in answer phrasing can swing scores without reflecting meaningful quality differences.

What to do about it

1. Check what each metric is actually measuring

   • Map each Ragas metric to a human-defined quality dimension.
   • If a metric doesn’t align with your goal, don’t use it as a primary KPI.

2. Build a small human-labeled set

   • Sample 50–200 examples.
   • Have humans rate them on the exact criteria you care about.
   • Compare Ragas against those labels to see where it breaks.

3. Use Ragas comparatively, not absolutely

   • It’s often better for:
     • comparing prompt versions
     • comparing retrievers
     • regression testing
   • Less reliable as a final “quality score.”

4. Tune thresholds per task

   • A 0.8 in one domain may be mediocre, while in another it may be excellent.
   • Calibrate against your own human ratings.

5. Add task-specific evaluation

   • For example:
     • exact answer correctness
     • citation accuracy
     • tool success
     • business rule compliance
     • refusal quality
   • These often correlate better with human judgment than generic metrics.

6. Inspect failure cases

   • Look at examples where Ragas and humans disagree.
   • Patterns usually emerge: missing nuance, overly harsh faithfulness scoring, wrong retrieval assumptions, etc.

Practical recommendation

Use a hybrid eval stack:

• Ragas for automated, scalable regression checks
• Human evals for calibration and periodic audits
• Task-specific rules/metrics for what matters operationally

If you want, I can help you:

• diagnose which Ragas metric is drifting from human judgment,
• design a human evaluation rubric,
• or create a calibration plan to make scores more aligned.