exploring-llm-clusters

name: exploring-llm-clusters description: 'Investigate AI observability clusters — understand usage patterns in AI/LLM traffic, compare cluster behavior, compute cost/latency metrics, and drill into individual traces within clusters.'

Exploring LLM clusters

Use this skill when investigating AI observability clusters — understanding what patterns exist in your AI/LLM traffic, comparing cluster behavior, and drilling into individual clusters.

Tools

How clustering works

PostHog clusters LLM traces (or individual generations) by embedding similarity. A Temporal workflow runs periodically or on-demand, producing cluster events stored as $ai_trace_clusters (trace-level) or $ai_generation_clusters (generation-level).

Each cluster event contains:

• $ai_clustering_run_id — unique run identifier (format: <team_id>_<level>_<YYYYMMDD>_<HHMMSS>[_<job_id>])
• $ai_clustering_level — "trace" or "generation"
• $ai_window_start / $ai_window_end — time window analyzed
• $ai_total_items_analyzed — number of traces/generations processed
• $ai_clusters — JSON array of cluster objects
• $ai_clustering_params — algorithm parameters used

Cluster object shape (inside $ai_clusters)

{
  "cluster_id": 0,
  "size": 42,
  "title": "User authentication flows",
  "description": "Traces involving login, signup, and token refresh operations",
  "traces": {
    "<trace_or_generation_id>": {
      "distance_to_centroid": 0.123,
      "rank": 0,
      "x": -2.34,
      "y": 1.56,
      "timestamp": "2026-03-28T10:00:00Z",
      "trace_id": "abc-123",
      "generation_id": "gen-456"
    }
  },
  "centroid_x": -2.1,
  "centroid_y": 1.4
}

• cluster_id: -1 is the noise/outlier cluster (items that didn't fit any cluster)
• Items in traces are keyed by trace ID (trace-level) or generation event UUID (generation-level)
• rank orders items by proximity to centroid (0 = closest)
• x, y are 2D coordinates for visualization (UMAP/PCA/t-SNE reduced)

Clustering jobs

Each team can have up to 5 clustering jobs. A job defines:

• name — human-readable label
• analysis_level — "trace" or "generation"
• event_filters — property filters scoping which traces are included
• enabled — whether the job runs on schedule

Default jobs named "Default - trace" and "Default - generation" are auto-created and disabled when a custom job is created for the same level.

Workflow: explore clusters

Step 1 — List recent clustering runs

posthog:execute-sql
SELECT
    properties.$ai_clustering_run_id as run_id,
    properties.$ai_clustering_level as level,
    properties.$ai_window_start as window_start,
    properties.$ai_window_end as window_end,
    toInt(properties.$ai_total_items_analyzed) as total_items,
    timestamp
FROM events
WHERE event IN ('$ai_trace_clusters', '$ai_generation_clusters')
    AND timestamp >= now() - INTERVAL 7 DAY
ORDER BY timestamp DESC
LIMIT 10

Step 2 — Get clusters from a specific run

posthog:execute-sql
SELECT
    properties.$ai_clustering_run_id as run_id,
    properties.$ai_clustering_level as level,
    properties.$ai_clustering_job_id as job_id,
    properties.$ai_clustering_job_name as job_name,
    properties.$ai_window_start as window_start,
    properties.$ai_window_end as window_end,
    toInt(properties.$ai_total_items_analyzed) as total_items,
    properties.$ai_clusters as clusters,
    properties.$ai_clustering_params as params
FROM events
WHERE event IN ('$ai_trace_clusters', '$ai_generation_clusters')
    AND properties.$ai_clustering_run_id = '<run_id>'
LIMIT 1

The clusters field is a JSON array. Parse it to see cluster titles, sizes, and descriptions.

Important: The clusters JSON can be very large (thousands of trace IDs with coordinates). When the result is too large for inline display, it auto-persists to a file. Use print_clusters.py from scripts/ to get a readable summary.

Step 3 — Compute metrics for clusters

For trace-level clusters, compute cost/latency/token metrics:

posthog:execute-sql
SELECT
    properties.$ai_trace_id as trace_id,
    sum(toFloat(properties.$ai_total_cost_usd)) as total_cost,
    max(toFloat(properties.$ai_latency)) as latency,
    sum(toInt(properties.$ai_input_tokens)) as input_tokens,
    sum(toInt(properties.$ai_output_tokens)) as output_tokens,
    countIf(properties.$ai_is_error = 'true') as error_count
FROM events
WHERE event IN ('$ai_generation', '$ai_embedding', '$ai_span')
    AND timestamp >= parseDateTimeBestEffort('<window_start>')
    AND timestamp <= parseDateTimeBestEffort('<window_end>')
    AND properties.$ai_trace_id IN ('<trace_id_1>', '<trace_id_2>', ...)
GROUP BY trace_id

For generation-level clusters, match by event UUID:

posthog:execute-sql
SELECT
    toString(uuid) as generation_id,
    toFloat(properties.$ai_total_cost_usd) as cost,
    toFloat(properties.$ai_latency) as latency,
    toInt(properties.$ai_input_tokens) as input_tokens,
    toInt(properties.$ai_output_tokens) as output_tokens,
    if(properties.$ai_is_error = 'true', 1, 0) as is_error
FROM events
WHERE event = '$ai_generation'
    AND timestamp >= parseDateTimeBestEffort('<window_start>')
    AND timestamp <= parseDateTimeBestEffort('<window_end>')
    AND uuid IN ('<gen_uuid_1>', '<gen_uuid_2>', ...)

Step 4 — Drill into specific traces

Once you've identified interesting clusters, use the trace tools to inspect individual traces:

posthog:query-llm-trace
{
  "traceId": "<trace_id_from_cluster>",
  "dateRange": {"date_from": "<window_start>", "date_to": "<window_end>"}
}

Investigation patterns

"What kinds of LLM usage do we have?"

1. List recent clustering runs (Step 1)
2. Load the latest run's clusters (Step 2)
3. Review cluster titles and descriptions — each represents a distinct usage pattern
4. Compare cluster sizes to understand traffic distribution

"Which cluster is most expensive / slowest?"

1. Load clusters from a run (Step 2)
2. Extract trace IDs from each cluster
3. Compute metrics per cluster (Step 3)
4. Aggregate: avg(cost), avg(latency), sum(cost) per cluster
5. Compare across clusters

"What's in this cluster?"

1. Load the cluster's traces (from the traces field)
2. Sort by rank (closest to centroid = most representative)
3. Inspect the top 3-5 traces via query-llm-trace to understand the pattern
4. Check the cluster title and description for the AI-generated summary

"Are there error-heavy clusters?"

1. Compute metrics (Step 3) with error_count
2. Calculate error rate per cluster: items_with_errors / total_items
3. Focus on clusters with high error rates
4. Drill into errored traces to find root causes

"How do clusters compare across runs?"

1. List multiple runs (Step 1)
2. Load clusters from each run
3. Compare cluster titles — similar titles across runs indicate stable patterns
4. Track cluster size changes to detect shifts in traffic patterns

Constructing UI links

• Clusters overview: https://app.posthog.com/ai-observability/clusters
• Specific run: https://app.posthog.com/ai-observability/clusters/<url_encoded_run_id>
• Cluster detail: https://app.posthog.com/ai-observability/clusters/<url_encoded_run_id>/<cluster_id>

Always surface these links so the user can verify visually in the PostHog UI.

Tips

• Always set a time range in SQL queries — cluster events without time bounds are slow
• Start with run listing to orient, then drill into specific clusters
• Cluster titles and descriptions are AI-generated summaries — verify by inspecting traces
• The noise cluster (cluster_id: -1) contains outliers that didn't fit any pattern
• Use llma-clustering-job-list to understand what clustering configs are active
• Trace IDs in clusters can be used directly with query-llm-trace for deep inspection
• For large clusters, inspect the top-ranked traces (closest to centroid) for representative examples