GenAI observability method

Reading mode

EngineerDecision-maker

Where this content fits

1. 1. DiscoverAI in cross-sectionthe five layers, where to look
2. 2. UnderstandUnderstanding generative AI observabilityconcepts and definitions
3. 3. PracticeLLM observability: the labshands-on, on a self-hosted stack
4. 4. Go to productionPlaybooks, then VictoriaMetrics for LLMsproduction stack, OpenTelemetry conventions, licenses
5. 5. MasterGenAI observability method (you are here)quality SLOs, quantified impact, compliance, RACI
6. 6. Go beyondBeyond the LLM: GPUs and model qualityGPU infrastructure, classic ML, statistical drift

Expert level: this method assumes the concepts of the guide and, ideally, hands-on practice from the labs; it is the site’s reference for quality SLOs, quantified impact, compliance (AI Act, evidence matrix) and organization (RACI).

Monitoring an LLM, an agent, a RAG and MCP

Framework, reference configurations, quality SLOs, quantified impact, compliance · Axes: reliability, availability, cost, compliance

Why start from silent failure

In my view, many GenAI observability guides are taxonomies that list what can be measured. This method starts from an operational principle, the silent failure model, and follows it down to implementation with reference configurations (not yet run end to end in a lab, each page says so), queries, probabilistic SLOs, a quantified cost model and a regulatory evidence matrix. It tries to answer a narrower question than “what can we measure?”: what to instrument first, for what reason, and what it costs to leave it out.

Contents

Part	Content
I. Framework	silent failure, four dimensions, five signals, OpenTelemetry GenAI
II. Architecture	unified telemetry plane, reference Collector, recording rules
III. Measurement grid	LLM, RAG, agent, MCP: what, why, impact if missing
IV. Quality SLOs	probabilistic SLIs, Wilson bound, burn rate, judge meta-observability
V. Quantified impact	cost model of not monitoring, worked example, prioritization
Calculator	put a figure on your blind spots
VI. Compliance	AI Act, NIS2, GDPR evidence matrix
VII. Organization	RACI, quality on-call
VIII. Anti-patterns	the pitfalls specific to the method; the others are in chapter 9 of the guide
IX. Implementation	seven-phase approach, reference lab (in preparation), checklists
Annexes	metrics, tools (pointer to the guide), method-specific terms, method for a dated price table

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Revised on 4 October 2026: page moved to MDX with the progression box, positioning sentence (expert level) and contents updated after removing duplicates with the guide.

BridgesWhat this page changes beyond the technical team.

BusinessValue, cost, risk

Part V sizes the cost of not monitoring in tokens and calls, to convert into euros with your own prices, and the calculator makes it presentable to finance.

OrganizationWho decides, who owns

Part VII gives a RACI over eight capabilities: instrumentation, service SLOs, quality SLOs, evaluations, FinOps, MCP security, audit and compliance, quality on-call.

PeopleTeams, on-call, skills

Quality on-call, with fallback procedures written before the incident, is, in my view, new for many teams.

Business linesProduct, finance, risk, customer

The evidence matrix in part VI ties each obligation (AI Act, NIS2, GDPR) to the signal that proves it: a tool to talk with compliance.

Why bridges matter →