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Documentation Index

Fetch the complete documentation index at: https://avocadostudioai.mintlify.app/llms.txt

Use this file to discover all available pages before exploring further.

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Chat Behavior Troubleshooting Playbook

This playbook is for investigating prompt failures, wrong operations, and regressions in the site editor assistant. For background on the editing pipeline, see How It Works. For the full telemetry event reference, see Chat Telemetry Events.

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What telemetry is captured

The orchestrator now emits structured chat telemetry events for each request:
  • received
  • forced_plan
  • plan_attempt_failed
  • plan_generated
  • plan_apply_failed
  • repair_attempt
  • repair_generated
  • result
Each event includes:
  • request id
  • session
  • requested/effective slug
  • model key/model used
  • planner source (openai or demo)
  • prompt hash (stable fingerprint)
  • prompt excerpt (short preview)
  • prompt length
  • intent/op types/op count (when available)
  • outcome + error category (when available)

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Persistence

Telemetry is persisted to NDJSON so it survives restarts.
  • Default file: .data/chat-telemetry.ndjson
  • Env override: CHAT_TELEMETRY_FILE
  • Disable persistence: CHAT_TELEMETRY_PERSIST=0
  • In-memory buffer size: CHAT_TELEMETRY_LIMIT (default 500)
On startup, orchestrator loads recent telemetry entries from disk.

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APIs for debugging

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1) Raw telemetry stream (filtered)

GET /telemetry/chat Query params:
  • limit (default 100, max 1000)
  • session
  • phase
  • outcome
Example: 
curl -s "http://127.0.0.1:4200/telemetry/chat?session=dev&limit=200" | jq

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2) Review summary for manual test runs

GET /telemetry/chat/review Query params:
  • limit (default 300, max 2000)
  • session
Returns:
  • analyzed count
  • applied/failed counts
  • failure rate
  • failure breakdown by outcome
  • failure breakdown by reason category
  • top failed prompts (grouped by prompt hash)
  • automatic recommendations
Example: 
curl -s "http://127.0.0.1:4200/telemetry/chat/review?session=dev&limit=500" | jq

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UI debug mode (for screenshots)

Enable debug metadata directly in assistant response cards:
  1. Open Content Studio settings (gear icon).
  2. Enable Debug mode.
  3. Run the prompt and capture screenshot.
Each assistant card includes a Debug panel with:
  • traceId
  • promptHash
  • outcome
  • reason category
  • intent
  • opCount and ops
  • prompt excerpt
Use this panel when sharing failures so engineering can reproduce and convert them into regression tests quickly.

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Standard workflow after manual UI testing

  1. Use a dedicated session for a test batch (for example: manual-2026-03-01-a).
  2. Run your manual prompts in UI.
  3. Pull review summary:
curl -s "http://127.0.0.1:4200/telemetry/chat/review?session=manual-2026-03-01-a&limit=1000" | jq
  1. Inspect top failures:
    • high schema_violation: normalization/repair gaps
    • high not_found: slug/block resolution gaps
    • high ambiguity: clarification prompts too weak
  2. Drill into raw events for one problematic prompt hash:
curl -s "http://127.0.0.1:4200/telemetry/chat?session=manual-2026-03-01-a&limit=1000" \
| jq '.rows | map(select(.promptHash=="<hash>"))'
  1. Convert top failed prompts into regression tests in:
    • apps/orchestrator/src/nlp-ops.test.ts
  • Always run manual tests with an explicit session id.
  • Keep a fixed prompt suite for weekly regression checks.
  • Add a test for every new recurring failed prompt family.
  • Track failure rate trend (/telemetry/chat/review) before/after changes.

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Quick checklist when behavior is wrong

  • Did the model fail to produce valid schema?
  • Did normalization repair aliases correctly?
  • Was deterministic fallback expected but not triggered?
  • Was the apply step blocked by not-found or no-effective-change?
  • Did clarification context leak across unrelated intents?