LLM Alerting Outages Playbook

Last month I woke to a page at 3 AM. Our checkout flow was blocked because the LLM started returning nonsense prices. I chased logs while the product manager calmly drank chai. He asked if the model was having a midlife crisis, and I joked back that maybe it wanted a sports car. I found myself comparing prompt versions and cost thresholds, while he asked if we could roll back to the calm old model. The whole team laughed between caffeine sips and frantic shell commands.

I write this because "LLM alerting outages" is a different problem than classic service outages. I have done LLM incident response work at places like Zomato and BrowserStack. I will share a practical playbook. This guide covers detection, metrics, alerting, debugging, and a clear incident runbook. I also explain how to use LaikaTest to verify alerts and run safe experiments. The focus is on real signals, not theory. I want teams to recover quickly and prevent repeats.

Why LLM Outages Are Different

LLMs fail in ways traditional services do not. A web server can crash and show a 500 error. An LLM can return a well-formed response that is wrong. That is a silent regression. It looks healthy on uptime checks, and the UI still shows a green dot. But users see garbage answers. This is the key risk.

Think of it like a restaurant. The kitchen is open, the lights are on, and the staff are at their stations. The food that comes out tastes wrong. The chef has swapped salt for sugar, or a recipe got changed. Customers do not know why; they just get bad meals. The restaurant is not "down," but it is failing at its core job.

LLM outages can be:

• Functional, where a response breaks a flow. For example, a price gone wrong that blocks checkout.

• Safety-related, where the model returns toxic or disallowed content.

• Quality degradations, where responses lose factual grounding or become inconsistent.

These outages often return 200 status codes. That means uptime monitors are not enough. You need signals beyond simple health checks. You also need fast ways to roll back or reroute traffic.

What Are the Problems with LLM?

• The main downfall is silent failures. LLMs can return plausible, wrong content.

• They hallucinate facts and invent details.

• They can drift after prompt or model updates.

• They are non-deterministic, so the same prompt can produce different answers.

• They can degrade in grounding, producing content that is not tied to data.

• They often have coverage gaps for niche or new queries.

• Integrations with tools can leak state or memory, causing weird behaviors.

• Monitoring and debugging are harder because errors are in content, not status codes.

Observable Signals for LLM Health

To catch these failures, we need many signals. No single metric is enough. Combine them like sensors on a car. Oil pressure, temperature, and brake lights together tell you more.

Key signals to collect:

• Latency percentiles and tail latencies.

• Error types from providers, like rate limit errors and auth failures.

• Grounding score, which measures factual support from retrieval or sources.

• Hallucination rate, measured by automated checks or human review.

• Cache hit ratio, to spot when cache misses spike.

• Tool errors, such as failed database calls in an agent.

• Response length and token patterns, for unusual token bursts.

• Provider metadata, like model version and region.

Why Combine Signals? Single signals can lie. A latency spike with no content issues might be a transient network glitch. A drop in grounding score with normal latency is likely a model regression. When multiple signals move together, the probability of a real outage rises.

Prioritize signals based on customer impact. For checkout or billing flows, grounding and correctness matter most. For chat features, hallucination and safety are top priorities.

Link: See the LLM Observability & Tracing pillar page for more on what to collect and how to trace requests.

Metrics, SLOs, and Measuring Failure Rate

SLOs must capture quality, not just availability. A model that answers wrong 5 times in 10,000 requests is not acceptable for billing. Define SLOs that include acceptable hallucination rates per 10k requests. Also include grounding and safety thresholds.

How to Compute Failure Rate:

1. Define failures. Include false positives, false negatives, and silent errors.

2. Count failures over a sliding window. For example, a 1-hour and a 24-hour window.

3. Compute failure rate as failures divided by total requests in the window.

4. Use cohorting by user segment, endpoint, or prompt version. This helps detect drift.

Include false positives. A detection that flags correct responses as bad must be counted. Also include silent errors, where the content is wrong but no error code was raised. That means automated content checks and sampling are essential.

Use sliding windows and cohorting to detect model drift. If failure rate rises only for a specific cohort, the problem may be with a prompt change or new data. Sliding windows help catch trends before they cross SLOs.

Analogy: This is like a vending machine. You measure how often it gives the wrong snack. If one slot misfires more after the machine was refilled, you focus on that slot. Metrics help you detect the bad slot early.

What Is the Failure Rate of LLM?

• It is the fraction of requests that fail an SLO. That includes incorrect answers, unsafe outputs, and undetected silent errors. Measure it per cohort and per time window.

Alerting Strategies Tuned for LLMs

LLM alerts need to be smarter. Do not alert on single noisy blips. Combine signals and alert on patterns.

Principles:

• Use multi-signal alerts. For example, alert when latency p95 rises, grounding falls, and hallucination rate increases.

• Set adaptive thresholds. Use baselines that account for seasonality.

• Alert on trends, not single points. For example, a 10% sustained rise over 30 minutes.

• Tier alerts by impact. Use P1 for customer blocking incidents, P2 for degraded quality, and P3 for minor regressions.

• Automate escalation. If an alert is P1 and not acknowledged within N minutes, escalate to on-call.

Analogy: Think of fire alarms that tell toast smoke apart from a real fire. Simple alarms that go off for every burnt toast cause panic. Smart alarms mute the kitchen during breakfast but still ring loud for real fires.

Anomaly Detection Techniques

You need robust anomaly detection to catch subtle drift. Use a mix of methods.

Techniques:

• Statistical baselines with rolling windows and seasonality. This catches sudden jumps.

• ML models that learn normal metric patterns. These help for complex signals.

• Semantic anomalies using embedding distance. Track distribution shifts in response embeddings.

• Prompt response drift. Measure distance between new responses and golden responses.

• Class collapse signals. Detect when outputs collapse to repetitive or generic answers.

Balance sensitivity and alert fatigue. Use rate limiting, silence windows, and grouped alerts. For example, only alert if 5 of 10 samples fail a content check.

Analogy: Think of airport security. They watch for unusual luggage patterns. A single odd bag is not enough. They look for patterns across many bags.

Link: See LLM Observability & Tracing pillar page for methods and examples.

Tracing and Request Level Observability

End-to-end tracing is critical. Trace from user prompt to final response. Include provider metadata, costs, and tool calls.

What to Capture:

• Payload hashes to identify similar requests.

• Model version and provider region.

• Prompt template and variables.

• Tool calls made by agents and their outcomes.

• Latency breakdown for each stage.

Make traces queryable and linkable from alerts. When an alert fires, you should open a trace and see the exact prompt, response, and tools used. That speeds debugging.

Analogy: This is like a parcel tracker that shows each handoff stage. You know which courier took the package and when it changed hands.

Link: See the LLM Observability & Tracing pillar page for tracing best practices.

LLM Incident Response Playbook

A clear playbook reduces chaos. Use a step-by-step runbook.

Runbook Steps:

1. Detect. Alerts fire from combined signals.

2. Triage. Classify impact and scope. Is it P1, P2, or P3?

3. Isolate. Stop sending traffic to the failing model if needed.

4. Mitigate. Reroute to a fallback model, enable cached responses, or throttle inputs.

5. Recover. Roll back prompt changes or switch to a previous model.

6. Validate. Run smoke tests and confirm user flows work.

7. Postmortem. Document causes, mitigation, and prevention.

Define roles and runways. Who owns triage, who handles communications, and who executes rollbacks. This prevents the late-night ping pong I experienced.

Mitigations to Have Ready:

• Reroute to a fallback model.

• Degrade features that depend on LLM.

• Serve cached responses for known prompts.

• Throttle new inputs to reduce error rate.

Analogy: This is like an emergency response team. Each person has a role and a checklist. Everyone knows who calls the ambulance.

Link: See AI Debugging & Reliability pillar page for incident templates and runbooks.

What Is the Downfall of LLM?

• The downfall is that they can fail silently and unpredictably. They can regress without errors. They need content-aware monitoring and careful incident playbooks.

Debugging Workflows for Common LLM Errors

Debugging needs method and discipline. Start with the impact and scope.

Steps:

1. Measure impact. How many users were affected, and which endpoints?

2. Reproduce with exact prompt, context, and model metadata.

3. Trace by trace debugging. Compare failing traces to golden traces.

4. Look for common causes.

Common Causes:

• Provider regressions after a model update.

• Prompt template changes pushed without tests.

• Input distribution shift. New user behavior or new data types.

• Memory leaks in tool integrations. State from past calls causing bad outputs.

Analogy: It is like diagnosing a car that stalls. You check fuel, spark, and air in order. That helps you narrow the cause fast.

Link: See AI Debugging & Reliability pillar page for debugging checklists and examples.

What Are the Problems with LLM?

• They are non-deterministic, they hallucinate, they drift, and they depend on context and data. Monitoring and testing are harder than for traditional services.

Data and Training Related Issues

Data problems show up as poor grounding and brittle behavior. When training data lacks coverage for certain queries, the model will invent answers.

Things to Monitor:

• Coverage and label quality in training and fine-tuning sets.

• Concept drift between training data and production inputs.

• Distribution of retrieval results for cases using RAG.

Mitigations:

• Augment data with examples that cover new cases.

• Use retrieval augmentation to provide grounding.

• Add negative examples to reduce unsafe or hallucinated outputs.

Analogy: This is like a chef running out of fresh ingredients. They rely on canned goods and the food loses taste. Better ingredients and recipes fix it.

Is There a Data Shortage for LLM?

• Sometimes yes. Gaps in coverage cause hallucinations and brittleness. You must monitor training data and patch gaps with targeted examples and retrieval.

Postmortem, RCA, and Preventing Repeat Outages

Postmortems must be evidence-based. Include traces, metric timelines, and mitigation steps. Avoid vague blame. Focus on facts and actions.

Turn Findings into Prevention:

• Better tests and smoke checks for model updates.

• More observability for the most critical prompts.

• Tighter SLOs for quality metrics.

• Automation for rollbacks and routing changes.

Create automated smoke tests and canarying for model updates. Run experiments on a small portion of traffic before full rollout.

Analogy: It is like a debrief after a rescue mission. You update the procedure so the next team does better.

Link: See LLM Observability & Tracing pillar page for postmortem templates and canary playbooks.

Tooling, Checklist, and Playbook Templates

Checklist Items to Keep Updated:

• Signal ownership and alert routing.

• Rollback plan and fallback models.

• Postmortem owner and timelines.

• Canary and smoke test definitions.

Recommended Integrations:

• Tracing and end-to-end observability.

• Anomaly detection for content metrics.

• Incident management and runbook automation.

How LaikaTest Fits: LaikaTest helps teams test and validate changes safely in production-like environments. Use it for regression testing, anomaly simulation, and replaying failing traces. It links prompt versions to results, so you can tell which change broke or fixed behavior.

Analogy: It is like a pilot checklist before every flight. You run the same checks each time, and you catch simple mistakes before they become emergencies.

Link: See AI Debugging & Reliability pillar page for templates and integrations.

Conclusion with LaikaTest

Key Takeaways:

• LLM outages are often silent. You must watch content and quality, not just uptime.

• Combine multiple signals and use cohorting to detect regressions fast.

• Define SLOs that include hallucination and grounding metrics.

• Use multi-signal alerts and tiered escalation.

• Trace every request, and make traces linkable from alerts.

• Have a clear incident playbook and runbook with roles and mitigations.

• Test model updates with canaries and automated smoke tests.

LaikaTest is a practical tool to help with several of these steps. It lets you run prompt A/B tests on real traffic. It ties exact prompt versions to outputs, tool calls, costs, and latency. Use LaikaTest to inject anomalies and verify alerts fire. The simple flow I recommend is this:

1. Capture a failing trace. Save the exact prompt and model metadata.

2. Use LaikaTest to replay the trace to a test harness.

3. Inject small anomalies or swap prompt variants to see which change fixes the issue.

4. Verify that monitoring signals and alerting rules detect the issue in the test setup.

5. Run your incident playbook steps with the test scenario to confirm runbook actions work.

6. Add the successful test as a regression check in CI.

This process helps teams prove that a change improved behavior instead of relying on "it felt better." It reduces the chance of silent regressions. It also gives concrete evidence for postmortems.

If you focus on signals, tracing, and disciplined incident response, most LLM outages will be manageable. Use LaikaTest to make experiments safe and repeatable. Add checks to CI and include LaikaTest outputs in postmortems. That will make your team faster at resolving incidents and better at preventing them.