> ## Documentation Index
> Fetch the complete documentation index at: https://docs.budecosystem.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Troubleshooting

> Common issues and fixes for evaluations and experiments

<Note>
  Use this guide to quickly diagnose issues while running evaluation experiments.
</Note>

## Quick Triage Flow

```mermaid theme={null}
flowchart TD
    A[Issue Detected] --> B{Where?}
    B -->|Hub| C[Check search + trait filters]
    B -->|Run| D[Check model/dataset selection]
    B -->|Results| E[Check tab and run state]
    C --> F[Retry]
    D --> F
    E --> F
```

## Dataset Discovery Issues

### No datasets shown in Evaluations Hub

**Possible causes**

* Search query is too restrictive.
* Trait filters exclude all results.

**Fixes**

1. Clear search input.
2. Remove all trait filters.
3. Reapply filters one by one.

## Run Launch Issues

### Run Evaluation button does not complete a run

**Possible causes**

* Required model or dataset selection is missing.
* Selected configuration is invalid for the chosen scope.

**Fixes**

1. Reopen run form and verify all selections.
2. Start with one trait and one dataset.
3. Retry with a known-good model target.

## Result Interpretation Issues

### Leaderboard has no useful comparison

**Possible causes**

* Too few completed runs.
* Models were evaluated on different scopes.

**Fixes**

1. Rerun candidates on the same traits/datasets.
2. Keep all comparisons in one experiment.

### Explorer data appears inconsistent with score

**Possible causes**

* Sampling differences across runs.
* Score is aggregate while Explorer is row-level.

**Fixes**

1. Review multiple rows, not a single sample.
2. Rerun to confirm consistency.

## Experiment Management Issues

### Hard to locate the right experiment

**Fixes**

* Use standardized tags and naming.
* Sort by creation date and filter by status/model.

### Too many failed runs

**Fixes**

* Reduce scope (fewer traits/datasets) to isolate failure.
* Rerun incrementally after each configuration change.

## Escalation Checklist

Before escalating internally, collect:

* Experiment name and run timestamp.
* Model, traits, and datasets selected.
* Observed status and screenshots of key tabs.
* Whether issue reproduces after rerun.