Anyone else think the score explanations feature is a bit gimmicky?

I've been conducting an extensive evaluation of Langfuse for the past six weeks, integrating it into a secondary analytics pipeline for a mid-sized SaaS product. The goal was to assess its capabilities for observability and evaluation against our existing Mixpanel/Amplitude stack for core product analytics and a custom-built system for LLM-specific metrics. A feature that immediately stands out, and is heavily marketed, is the automated "score explanations" for evaluations. After rigorous testing, I've concluded that this feature, while conceptually appealing, suffers from significant practical and methodological flaws that render it of limited utility for serious optimization work.

My primary concern is the lack of statistical rigor and actionable insight. The explanations are generated by another LLM call (to OpenAI or Anthropic) that simply parses the trace data and the score you provided. It doesn't perform any causal or correlational analysis. For instance, consider a scenario where you score a chain-of-thought trace on a 1-5 scale for correctness.

```python
# Example of what Langfuse does internally for explanation
prompt_for_explanation = f"""
Trace ID: {trace_id}
User Input: {input}
Model Output: {output}
Assigned Score: {score} (Scale: 1-5)
Please provide a concise explanation for why this score was given.
"""
```
The resulting explanation is a post-hoc narrative, not an analytical finding. It might state: "The score of 4 was given because the answer correctly identifies the key factors but misses one minor detail." This is merely a rephrasing of the human rater's implicit reasoning, offering no new information about *which* trace attributes (latency, token usage, specific intermediate steps, cost) actually correlate with high or low scores.

The critical shortcomings from an experimentation perspective are:
* **No Cohort or Segmentation Analysis:** It cannot tell you if explanations scoring poorly consistently have higher token counts or originate from a specific prompt template variant.
* **No Statistical Testing:** There is no indication of whether the difference in a given metric (e.g., average cost) between high-scoring and low-scoring traces is statistically significant or likely due to random variation.
* **Surface-Level:** The explanation is confined to a single trace. It cannot reference population-level data, which is the cornerstone of any meaningful A/B testing or optimization funnel.
* **Illusory Insight:** The feature creates an illusion of understanding, potentially leading teams to believe they have diagnosed an issue when they have only narrated it.

For a platform built on the premise of data-driven LLM development, this feels like a missed opportunity. A truly valuable feature would leverage the aggregated trace data to perform actual analysis. For example:
1. Run a regression analysis to surface which trace metrics (latency, cost, steps) are most predictive of a high manual score.
2. Provide a dashboard comparing key metrics between score cohorts (e.g., scores 1-2 vs. scores 4-5) with p-values.
3. Automatically flag traces where the explanation's stated reason (e.g., "too long") conflicts with the quantitative data (e.g., trace was actually in the shortest decile for token count).

Without this level of integration between the qualitative "explanation" and the hard quantitative data Langfuse already collects, the feature remains a gimmick—a nice-to-have for generating reports but insufficient for informing iterative improvements or hypothesis testing. I'm curious if others have attempted to use this feature for actual decision-making and what their experiences have been. Have you found a way to integrate it into a rigorous optimization loop, or do you also treat it as merely a documentation aid?