Concept card

LLM Observability — Tracing, Token Accounting, Hallucination Detection

As of 2026-07-24T14:00:00Z

Edited by Cédric-Olivier Mâry, sole editor and publication director — 27 years in the SAP ecosystem. How these pages are produced, and where automation is used.

What is LLM Observability — Tracing, Token Accounting, Hallucination Detection?

Traditional APM tools can't answer whether an LLM's answer was actually right — observability platforms fill that gap by sampling 1-5% of traffic plus 100% of the lowest-confidence answers against a judge model.

LLM observability is the discipline of seeing, for every model call a production system makes, what happened, why it happened, what it cost, and whether the answer was actually right. Conventional application performance monitoring answers the first two questions poorly and the third and fourth not at all — it was built for deterministic code paths, not for a system whose output varies from one nearly-identical call to the next. A newer generation of purpose-built platforms has grown up specifically to fill that gap, extending general distributed-tracing standards with attributes designed for generative AI: which model served the request, how many input and output tokens it consumed, what it cost, and how long it took.

The Four Surfaces That Matter

Distributed tracing of every model call is the foundation. Each user request becomes a tree of linked steps — user input, any retrieval calls, a reranking step, the model call itself, any tool calls the model triggers, and the final response — captured as a connected trace rather than a set of disconnected log lines. Without that trace, debugging a wrong or strange answer in production is close to guesswork, because there is no way to see which upstream step actually produced the bad input.

Why it matters

Four surfaces are non-negotiable: distributed tracing of the full span tree (input → retriever → reranker → model → tools → response), per-call token accounting tagged by tenant/workflow/model, sampled quality evaluation, and hallucination/groundedness detection.
The 2026 OpenTelemetry GenAI semantic-convention spec standardises span attributes like gen_ai.usage.input_tokens, adopted by every serious observability vendor (Langfuse, Arize Phoenix, LangSmith, Helicone).
Groundedness scoring catches RAG-specific failures (do the answer's claims appear in retrieved context?) while toxicity/PII/jailbreak classifiers catch safety failures — two distinct failure classes needing two distinct checks.

Key points

OpenTelemetry GenAI semantic conventions are the 2026 industry standard: `gen_ai.system`, `gen_ai.request.model`, `gen_ai.usage.input_tokens/output_tokens`, etc.
Distributed tracing of the full request span tree (input → retriever → reranker → model → tools → response) is non-negotiable for debugging.
Per-call token accounting tagged with tenant + workflow + model is the FinOps substrate (companion concept C249).
Quality evaluation: sample 1-5% of production traffic, replay against LLM-as-judge or golden dataset, trend the score, alert on drift.
Hallucination detection: groundedness scoring for RAG; toxicity/PII/jailbreak classifiers for safety; sample 100% of low-confidence answers.
Vendor landscape: Langfuse, Arize Phoenix, LangSmith, Weights & Biases Weave, Helicone — pick one and integrate via OTLP.
LLM Observability — Tracing, Token Accounting, Hallucination Detection is mastered only when it changes a named buyer decision.
Start with the semantic contract and control model before demonstrating the tool.
Use current SAP, analyst, study, KG, and news signals as evidence, not decoration.
Separate verified facts from directional trends and modeled assumptions.

Terms used on this page

OpenTelemetry GenAI: Semantic-convention extension of the OpenTelemetry standard defining standard span attributes for generative-AI calls (model, tokens, cost, tools).
LLM-as-judge: Evaluation pattern where a separate LLM scores the quality of another LLM's output against a rubric; cheaper than human review, noisier than human review.
Groundedness: RAG-specific quality metric: do the claims in the answer appear in the retrieved context? Low groundedness = hallucination.
OTLP: OpenTelemetry Protocol — the wire protocol for shipping traces/metrics/logs from instrumented apps to collectors and backends.
Decision owner: The accountable person who accepts the trade-off and funds the next action.
Semantic contract: The shared definition of business terms, metrics, entities, and access rules used by tools and teams.
Control plane: The layer that applies policy, access, lineage, monitoring, and escalation across the operating model.
Evidence grade: A label that separates verified fact, directional signal, modeled assumption, and field observation.

Sources

Full card available to members. What the full card adds: the full decision framework · the SAP vs Snowflake / Databricks / Fabric comparison · the common pitfalls and their fix · the cheat sheet · the architecture schemas · the code blocks · the facts worth quoting.

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