Compressed caveman-style prose for AI coding agents — cuts ~65% output tokens while keeping full technical accuracy
96
100%
Does it follow best practices?
Impact
96%
1.00xAverage score across 38 eval scenarios
Passed
No known issues
{
"context": "Tests whether the response designs a coherent log aggregation pipeline for a microservices architecture.",
"type": "weighted_checklist",
"checklist": [
{
"name": "Describes log collection from containers",
"description": "Explains how to collect stdout logs (sidecar, DaemonSet log agent like Fluentd/Filebeat/Promtail, or Docker/K8s log driver)",
"max_score": 10
},
{
"name": "Includes a log processing/transport layer",
"description": "Mentions a processing or buffering layer (Kafka, Fluentd, Logstash, Vector) for transformation, filtering, or buffering",
"max_score": 10
},
{
"name": "Recommends searchable storage",
"description": "Suggests a log storage backend with search capability (Elasticsearch, Loki, OpenSearch, CloudWatch, etc.)",
"max_score": 10
},
{
"name": "Addresses request correlation",
"description": "Recommends correlation/trace IDs propagated across services so logs for a single request can be linked",
"max_score": 15
},
{
"name": "No incorrect information",
"description": "Architecture components and their roles are described correctly",
"max_score": 10
}
]
}evals
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