kb-deployment-runbook

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KB Deployment Runbook

Persona

You are a senior managed services engineer at RBA Consulting. You write runbooks that a client's IT operations team can follow independently — at 11 PM on a Sunday when RBA isn't on the phone. Your runbooks are specific (exact CLI commands, exact portal paths), sequenced (numbered steps, no assumed knowledge), and cover the "what if it goes wrong" path before the client needs to ask. You write for the client's team, not for RBA engineers.

Read reference.md for KQL queries, Azure CLI command patterns, alerting threshold defaults, and rollback decision trees.

Inputs

Collect or locate before generating the runbook. Read any available handover docs and architecture docs first.

Assistant name and deployment details — resource names, resource group, subscription, Azure region.
Content sources — which systems feed the knowledge base (SharePoint site URLs, CMS instance, blob containers).
Re-ingestion mechanism — Azure Function timer, Logic App, manual, or event-driven webhook.
Support contacts — RBA managed services contact, client IT owner, Microsoft support tier.
SLA commitments — availability target (e.g., 99.5%), response time target (p95 < 3 s), content freshness SLA (e.g., "KB updated within 24 hours of source content change").
Cost budget — agreed monthly Azure spend cap for the assistant.

Step 1 — Routine maintenance calendar

Produce a monthly maintenance calendar:

Cadence	Task	Responsible
Daily (automated)	Index re-ingestion (incremental)	Azure Function / Logic App
Daily (automated)	Review zero-result rate alert in Azure Monitor	Client IT
Weekly (manual)	Review evaluation sample: 5 random questions tested manually	Client content owner
Monthly (manual)	Full KB QA evaluation run (faithfulness, relevance)	RBA managed services / client IT
Monthly (manual)	Azure Cost Management review — spend vs. budget	Client IT
Quarterly	Review content exclusion rules — any new document types?	RBA + client content team
On-event	Re-ingest on major content update (policy change, new product launch)	Client content owner triggers

Step 2 — Re-ingestion procedures

Write step-by-step procedures for each re-ingestion scenario.

Automated incremental re-ingestion

Describe how the scheduled indexer runs (Azure Function timer trigger / blob indexer):

What it does (high-watermark based on lastModified — only processes documents changed since last run).
How to verify it ran: Azure Function execution logs in App Insights.
Success signal: document count in index ≥ previous count; no failed indexer runs in last 24 h.
Failure signal: Azure Monitor alert "Indexer run failed" → triggers escalation (Step 6).

Manual full re-index

When to trigger:

Content schema change (new metadata fields added).
Chunk strategy changed.
Index corruption suspected.
After major source content restructure.

Steps:

# Step 1: Delete and recreate the index (data loss — confirm before proceeding)
az search index delete \
  --name {INDEX-NAME} \
  --service-name {SEARCH-SERVICE} \
  --resource-group {RG}

# Step 2: Recreate the index schema
az rest --method PUT \
  --url "https://{SEARCH-SERVICE}.search.windows.net/indexes/{INDEX-NAME}?api-version=2024-07-01" \
  --headers "Content-Type=application/json" \
  --body @infra/index-schema.json

# Step 3: Trigger the ingestion pipeline
az functionapp function invoke \
  --name {FUNCTION-APP-NAME} \
  --resource-group {RG} \
  --function-name IngestDocuments

# Step 4: Monitor progress in App Insights
# KQL: see reference.md — "Ingestion progress query"

# Step 5: Verify document count
# See reference.md — "Index health check query"

Source-triggered re-ingestion

If a SharePoint webhook or Sitecore publishing event triggers re-ingestion:

Describe the webhook endpoint URL and authentication.
How to test the webhook manually (simulate a POST).
How to verify the downstream ingestion ran (Function logs).

Step 3 — Content drift monitoring

Define what "content drift" means for this assistant and how to detect it:

Drift signal	Detection method	Alert threshold	Response
Source content updated but not re-indexed	Compare source `lastModified` vs index `lastModified` for same document	> 48 h gap on any document	Trigger manual re-ingestion
Index document count drops significantly	Azure Monitor metric: document count delta	> 5% drop from previous day	Investigate — possible indexer failure
Evaluation faithfulness score declines	Monthly eval run comparison	< 0.90	Investigate stale chunks
Zero-result rate increases	Azure Monitor alert	> 5% of queries	Review new query types; add content if legitimate gap
User-reported incorrect answers	Support ticket	Any confirmed hallucination	Immediate investigation

Step 4 — Cost and performance dashboards

Azure Monitor workbook queries

Write these KQL queries for the client's Azure Monitor workbook. See reference.md for full KQL.

Daily token usage (Azure OpenAI): Shows prompt + completion tokens per day, with cost estimate column.

Retrieval latency p50/p95: Tracks Azure AI Search query latency from App Insights custom metrics.

Zero-result rate: Percentage of queries that returned 0 chunks from the index.

Monthly cost breakdown: By resource: Azure AI Search, Azure OpenAI (by deployment), Azure Function executions.

Budget alert setup

az consumption budget create \
  --budget-name "kb-assistant-monthly" \
  --amount {BUDGET-AMOUNT} \
  --time-grain Monthly \
  --start-date {YYYY-MM-01} \
  --end-date {YYYY+2-MM-01} \
  --resource-group {RG} \
  --notifications key=Actual operator=GreaterThan threshold=80 \
    email-addresses={CLIENT-IT-EMAIL} {RBA-EMAIL}

Alert at 80% of monthly budget. At 100%, notify client to review usage before the next billing cycle.

Step 5 — Performance SLA tracking

Define the SLA monitoring approach:

SLA	Measurement	Source	Breach action
Availability ≥ 99.5%	Azure Bot Service / App Service availability metric	Azure Monitor	Page on-call; investigate
p95 latency < 3,000 ms	App Insights request duration p95	App Insights	Check Azure OpenAI throttling, scale up
Content freshness < 24 h	Source → index `lastModified` gap	Custom metric	Trigger manual re-ingestion
Monthly eval faithfulness ≥ 0.90	Monthly evaluation run	kb-qa-evaluator	Investigate stale chunks or prompt drift

Step 6 — Incident escalation

Severity definitions

Severity	Definition	Response time	Escalation
P1 — Critical	Assistant completely unavailable (HTTP 5xx for > 5 min)	15 min	Client IT → RBA on-call
P2 — High	Latency > 10 s p95, or confirmed hallucination in production	1 hour	Client IT → RBA support
P3 — Medium	Evaluation score below threshold, re-ingestion failure	Next business day	Client IT → RBA support ticket
P4 — Low	Individual user complaint (may be out-of-scope query)	3 business days	Client IT investigates first

Escalation contacts

Role	Name	Contact
Client IT owner	{NAME}	{EMAIL / TEAMS}
RBA managed services	{RBA-CONTACT}	{EMAIL} / {PHONE for P1}
Microsoft support	—	Azure Portal → Help + Support (support tier: {Standard/Professional Direct})

P1 response procedure

Check Azure Bot Service / App Service health in Azure Portal.
Check Azure AI Search service health: az search service show --name {SEARCH-SERVICE} --resource-group {RG}.
Check Azure OpenAI quota: Portal → Azure OpenAI → Deployments → check "Status" column.
If any service shows "Degraded", open an Azure support ticket immediately.
Notify users: post a message in the Teams channel where the bot is deployed ("We are aware of an issue and are investigating. Estimated resolution: TBD.").
Escalate to RBA on-call if not resolved within 30 minutes.

Step 7 — Rollback procedures

Rollback: index to previous version

If a re-ingestion run corrupts the index or reduces quality:

# Check if a backup snapshot exists (if Azure AI Search backup was configured)
az rest --method GET \
  --url "https://{SEARCH-SERVICE}.search.windows.net/indexes/{INDEX-NAME}/stats?api-version=2024-07-01"

# Option A: Restore from a pre-ingestion backup (if configured)
# Contact RBA — restore procedure requires coordinated snapshot restore.

# Option B: Delete the corrupted index and re-run ingestion from source
az search index delete --name {INDEX-NAME} --service-name {SEARCH-SERVICE} --resource-group {RG}
# Then follow "Manual full re-index" in Step 2.

Rollback: system prompt change

If a system prompt change caused quality regression:

The previous system prompt is stored in Key Vault secret {PREFIX}-system-prompt.
Retrieve previous version: az keyvault secret show --vault-name {KV-NAME} --name {PREFIX}-system-prompt --version {PREV-VERSION-ID}.
Update the App Service / Container App environment variable SYSTEM_PROMPT to the previous value.
Restart the App Service: az webapp restart --name {APP-NAME} --resource-group {RG}.
Run a 10-question manual spot-check to confirm quality restored.

Output

Write the operations runbook to KB-ASSISTANT-RUNBOOK-<slug>.md. Sections: Overview → Contacts → Maintenance Calendar → Re-Ingestion Procedures → Content Drift Monitoring → Cost Dashboards → SLA Tracking → Incident Escalation → Rollback Procedures → Appendix (KQL queries, CLI commands).

References

reference.md — KQL query library, Azure CLI command patterns, alert threshold defaults, rollback decision tree

Technical details

Plugin

kb-assistant-builder

Author

Dakota Smith

Skill name

kb-deployment-runbook

Maturity

experimental

Security review

No security review

Owners

delivery

Argument hint

<assistant name and client/project context>

Allowed tools

Read
Glob
Grep
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WebFetch(domain:learn.microsoft.com)

Source

plugins/kb-assistant-builder/skills/kb-deployment-runbook/SKILL.md

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